Abrégé

Systems and methods are provided for determining an optimized probe set. The method proceeds by obtaining a set of probes, where each probe has a respective concentration. The set of probes is assayed against a sample library, and at least i) a respective recovery rate for each probe in the set of probes, and ii) a median recovery rate for the set of probes are obtained. Modify the respective concentration of each probe that does not satisfy predetermined recovery rate threshold. Reevaluate the set of probes against the sample library. Repeat the modifying and reevaluation until the respective updated recovery rate for each probe in the updated set of probes satisfies the predetermined recovery rate threshold, thereby providing the optimized set of probes for the sample library.

Classes IPC  ?

• C12Q 1/6874 - Méthodes de séquençage faisant intervenir des réseaux d’acides nucléiques, p. ex. séquençage par hybridation [SBH]
• C12Q 1/6883 - Produits d’acides nucléiques utilisés dans l’analyse d’acides nucléiques, p. ex. amorces ou sondes pour les maladies provoquées par des altérations du matériel génétique
• C40B 40/06 - Bibliothèques comprenant des nucléotides ou des polynucléotides ou leurs dérivés

Abrégé

A method involves receiving a whole-slide image, processing it with a machine learning model to generate a prediction, determining attention scores for image tiles, selecting a subset based on these scores, and generating a pass/fail indication. A system includes processors and memory to perform these steps. A non-transitory computer-readable medium contains instructions for executing these processes.

Classes IPC  ?

• G06V 10/776 - ValidationÉvaluation des performances
• G06V 10/82 - Dispositions pour la reconnaissance ou la compréhension d’images ou de vidéos utilisant la reconnaissance de formes ou l’apprentissage automatique utilisant les réseaux neuronaux
• G06V 20/69 - Objets microscopiques, p. ex. cellules biologiques ou pièces cellulaires

Abrégé

Methods, systems, and software for estimating circulating tumor fraction are provided. A first plurality of nucleic acid sequences for a plurality of loci in genomic DNA from a solid tumor sample is obtained. A second plurality of nucleic acid sequences for a plurality of cell-free DNA fragments obtained from a liquid biopsy sample from the same subject is obtained. One or more somatic mutations is identified in the first plurality of nucleic acid sequences. A variant allele frequency (VAF) is determined for each somatic mutation based on a frequency of the respective somatic mutation in the liquid biopsy sample and a frequency of the corresponding wild type allele in the liquid biopsy sample, thereby determining a set of VAFs. An estimate of the circulating tumor fraction for the test subject is determined based on the set of VAFs for the one or more somatic mutations.

Classes IPC  ?

• G16B 30/10 - Alignement de séquenceRecherche d’homologie
• C12Q 1/6816 - Tests d’hybridation caractérisés par les moyens de détection
• C12Q 1/6886 - Produits d’acides nucléiques utilisés dans l’analyse d’acides nucléiques, p. ex. amorces ou sondes pour les maladies provoquées par des altérations du matériel génétique pour le cancer
• G16B 20/10 - Ploïdie ou détection du nombre de copies
• G16B 20/20 - Détection d’allèles ou de variantes, p. ex. détection de polymorphisme d’un seul nucléotide
• G16H 50/20 - TIC spécialement adaptées au diagnostic médical, à la simulation médicale ou à l’extraction de données médicalesTIC spécialement adaptées à la détection, au suivi ou à la modélisation d’épidémies ou de pandémies pour le diagnostic assisté par ordinateur, p. ex. basé sur des systèmes experts médicaux

Produits et services

Providing online non-downloadable software using artificial intelligence for healthcare providers to provide healthcare services to patients, including accessing electronic medical records and online healthcare information to provide optimized healthcare services to patients, reviewing patient health care metrics and data, and medical transcription and charting services for creating patient medical records and summaries

Produits et services

Medical research; Medical and scientific research in the field of genomics; genomic sequencing and genomic analysis. Medical services; Genetic and health information analysis and diagnostics for medical purposes.

Produits et services

Providing temporary use of non-downloadable computer software for analyzing health information in support of making medical decisions in the medical field; providing medical and scientific research information in the fields of oncology and customized genetic research; providing temporary use of non-downloadable computer software for analyzing and predicting nucleic acid yield. Medical services; providing medical information in the field of oncology; medical services in the field of oncology diagnostics and testing; providing medical diagnostic testing that analyzes patient samples and predicts nucleic acid yield.

Produits et services

(1) Medical research; Medical and scientific research in the field of genomics; genomic sequencing and genomic analysis. (2) Medical services; Genetic and health information analysis and diagnostics for medical purposes.

Produits et services

(1) Providing temporary use of non-downloadable computer software for analyzing health information in support of making medical decisions in the medical field; providing medical and scientific research information in the fields of oncology and customized genetic research; providing temporary use of non-downloadable computer software for analyzing and predicting nucleic acid yield. (2) Medical services; providing medical information in the field of oncology; medical services in the field of oncology diagnostics and testing; providing medical diagnostic testing that analyzes patient samples and predicts nucleic acid yield.

Abrégé

Systems and methods for distributing diagnostic prediction models. The system may receive, from an application provider, one or more diagnostic prediction models, each model trained to generate a respective diagnostic prediction based upon genomic data. Adaptation factors may be used to transform a target genomic dataset to conform to a dataset-specific nature of a reference genomic dataset of the model. The system may display via a graphical user interface, one or more representations corresponding to the diagnostic prediction models, and verify a diagnostic prediction model for an application consumer based upon receiving information corresponding to an application consumer genomic dataset. The system may authorize the diagnostic prediction model for distribution to the application consumer, and provide the diagnostic prediction model and the corresponding adaptation factors to the application consumer.

Classes IPC  ?

• G16B 40/20 - Analyse de données supervisée

Abrégé

Systems and methods for distributing diagnostic prediction models. The system may receive, from an application provider, one or more diagnostic prediction models, each model trained to generate a respective diagnostic prediction based upon genomic data. Adaptation factors may be used to transform a target genomic dataset to conform to a dataset-specific nature of a reference genomic dataset of the model. The system may display via a graphical user interface, one or more representations corresponding to the diagnostic prediction models, and verify a diagnostic prediction model for an application consumer based upon receiving information corresponding to an application consumer genomic dataset. The system may authorize the diagnostic prediction model for distribution to the application consumer, and provide the diagnostic prediction model and the corresponding adaptation factors to the application consumer.

Classes IPC  ?

• G06N 5/022 - Ingénierie de la connaissanceAcquisition de la connaissance
• G16H 10/20 - TIC spécialement adaptées au maniement ou au traitement des données médicales ou de soins de santé relatives aux patients pour des essais ou des questionnaires cliniques électroniques
• G16H 50/20 - TIC spécialement adaptées au diagnostic médical, à la simulation médicale ou à l’extraction de données médicalesTIC spécialement adaptées à la détection, au suivi ou à la modélisation d’épidémies ou de pandémies pour le diagnostic assisté par ordinateur, p. ex. basé sur des systèmes experts médicaux

Produits et services

Downloadable software and mobile applications for use in analyzing health information in support of making clinical decisions in the medical field; downloadable software and mobile applications for use as a smart digital assistant for connecting, operating, integrating, controlling, and managing databases in the field of electronic health records for analyzing health information in support of making clinical decisions in the medical field. providing online non-downloadable software for use in analyzing health information in support of making clinical decisions in the medical field; providing online nondownloadable software for use as a smart digital assistant for connecting, operating, integrating, controlling, and managing databases in the field of electronic health records for analyzing health information in support of making clinical decisions in the medical field.

Abrégé

A method includes receiving transcriptomic data; predicting standard deviation values for each gene expression values; for each sample: computing a simulated expression value, classifying the simulated expression values, computing confidence scores; and flagging the sample; and storing the confidence scores. A computing system includes a processor; and a memory having stored thereon instructions that when executed, cause the computing system to: receive transcriptomic data; predict standard deviation values for each gene expression values; for each sample: compute a simulated expression value, classify the simulated expression values, compute confidence scores; and flag the sample; and store the confidence scores. A computer-readable media includes non-transitory computer-readable instructions that, when executed, cause a computer to: receive transcriptomic data; predict standard deviation values for each gene expression values; for each sample: compute a simulated expression value, classify the simulated expression values, compute confidence scores; and flag the sample; and store the confidence scores.

Classes IPC  ?

• G16B 5/00 - TIC spécialement adaptées à la modélisation ou aux simulations dans la biologie des systèmes, p. ex. réseaux de régulation génétique, réseaux d’interaction entre protéines ou réseaux métaboliques
• G16B 40/00 - TIC spécialement adaptées aux biostatistiquesTIC spécialement adaptées à l’apprentissage automatique ou à l’exploration de données liées à la bio-informatique, p. ex. extraction de connaissances ou détection de motifs

Abrégé

System and methods for visualizing a cancer in a subject are provided herein. Medical images of a three-dimensional region of interest of the subject are obtained. The medical images are segmented by assigning labels corresponding to tissue types for each of a plurality of sets of one or more pixels in the medical images. Masks are generated for the tissue types based on the segmentation labels. A visual representation of tumor tissue is generated based on a corresponding mask for the tumor tissue. A visual representation of a skeleton of the subject is also generated based on the medical images. The visual representation of the tumor tissue and the visual representation of the skeleton of the subject is displayed in a single image, in a same spatial orientation as in the set of medical images, and at a same relative size as in the set of medical images.

Classes IPC  ?

• G16H 30/40 - TIC spécialement adaptées au maniement ou au traitement d’images médicales pour le traitement d’images médicales, p. ex. l’édition
• A61B 6/00 - Appareils ou dispositifs pour le diagnostic par radiationsAppareils ou dispositifs pour le diagnostic par radiations combinés avec un équipement de thérapie par radiations
• A61B 6/03 - Tomographie informatisée
• G06T 5/70 - DébruitageLissage
• G06T 7/00 - Analyse d'image
• G06T 7/11 - Découpage basé sur les zones
• G06T 7/174 - DécoupageDétection de bords impliquant l'utilisation de plusieurs images
• G06T 7/62 - Analyse des attributs géométriques de la superficie, du périmètre, du diamètre ou du volume
• G06T 17/20 - Description filaire, p. ex. polygonalisation ou tessellation
• G16H 15/00 - TIC spécialement adaptées aux rapports médicaux, p. ex. leur création ou leur transmission

Abrégé

A computer system is disclosed that evaluates efficacy of a cancer treatment in a subject undergoing or having completed treatment. The system executes a method comprising obtaining first genomic epigenetic sequencing data of the cell-free DNA in a blood sample of the subject. The method further obtains second genomic sequencing data of the cell-free DNA. The first and second data is mapped to locations in a reference genome. Absence or presence of at least a first genomic alteration, in a plurality of genomic alterations, is determined based on at least the second data and the mapped locations of the second data. A structured data report is securely communicated, through a network connection, providing an assessment of the cancer treatment efficacy for the subject responsive to at least the epigenetic patterns of the cell-free DNA and the absence or presence of at least the first genomic alteration.

Classes IPC  ?

• G16H 50/70 - TIC spécialement adaptées au diagnostic médical, à la simulation médicale ou à l’extraction de données médicalesTIC spécialement adaptées à la détection, au suivi ou à la modélisation d’épidémies ou de pandémies pour extraire des données médicales, p. ex. pour analyser les cas antérieurs d’autres patients
• G16B 50/30 - Entreposage de donnéesArchitectures informatiques
• G16H 10/60 - TIC spécialement adaptées au maniement ou au traitement des données médicales ou de soins de santé relatives aux patients pour des données spécifiques de patients, p. ex. pour des dossiers électroniques de patients
• G16H 20/00 - TIC spécialement adaptées aux thérapies ou aux plans d’amélioration de la santé, p. ex. pour manier les prescriptions, orienter la thérapie ou surveiller l’observance par les patients
• G16H 50/20 - TIC spécialement adaptées au diagnostic médical, à la simulation médicale ou à l’extraction de données médicalesTIC spécialement adaptées à la détection, au suivi ou à la modélisation d’épidémies ou de pandémies pour le diagnostic assisté par ordinateur, p. ex. basé sur des systèmes experts médicaux

Abrégé

Systems and methods for determining a blood tumor mutational burden (bTMB) for a test subject are provided in which there is obtained, from a panel-enriched sequencing reaction, a plurality of nucleic acid sequences. The plurality of nucleic acid sequences comprises a corresponding sequence for each cell-free DNA fragment in a plurality of cell-free DNA fragments obtained from a liquid biopsy sample from the test subject. Each respective cell-free DNA fragment in the plurality of cell-free DNA fragments corresponds to a respective probe sequence in a plurality of probe sequences used to enrich cell-free DNA fragments in the liquid biopsy sample in the panel-enriched sequencing reaction. There is determined, using the panel-enriched sequencing reaction, that a circulating tumor fraction (ctFE) is above a threshold ctFE value. Responsive to this determination, the bTMB is calculated for the test subject from the panel-enriched sequencing reaction and reported.

Classes IPC  ?

• G16B 20/20 - Détection d’allèles ou de variantes, p. ex. détection de polymorphisme d’un seul nucléotide
• G16H 10/20 - TIC spécialement adaptées au maniement ou au traitement des données médicales ou de soins de santé relatives aux patients pour des essais ou des questionnaires cliniques électroniques
• G16H 10/60 - TIC spécialement adaptées au maniement ou au traitement des données médicales ou de soins de santé relatives aux patients pour des données spécifiques de patients, p. ex. pour des dossiers électroniques de patients
• G16H 20/10 - TIC spécialement adaptées aux thérapies ou aux plans d’amélioration de la santé, p. ex. pour manier les prescriptions, orienter la thérapie ou surveiller l’observance par les patients concernant des médicaments ou des médications, p. ex. pour s’assurer de l’administration correcte aux patients

Produits et services

Downloadable software in the nature of a mobile application using artificial intelligence (AI) for use in providing personal health care and mental health support services to patients, namely, assisting patients with tracking symptoms and health metrics, providing access to medical records and health information, communicating with healthcare providers, preparing for medical appointments, and providing personalized health insights to individual patients. Software as a service (SAAS) featuring online non-downloadable software for use in providing recommendations for patient treatment and care in the field of mental health; software as a service (SAAS) featuring artificial intelligence for machine learning and predictive analytics to support clinical data platforms for patient diagnosis and treatment in the field of mental health. Medical services; providing medical information and patient health information; providing medical information to patients using artificial intelligence to provide personalized health insights for individual patients.

Produits et services

Computerized database management and data processing services in the fields of oncology and customized genetic research. Providing temporary use of non-downloadable computer software for electronic data capture and data management in the fields of oncology and customized genetic research; Providing medical and scientific research information in the fields of oncology and customized genetic research.

Produits et services

(1) Computerized database management and data processing services in the fields of oncology and customized genetic research. (2) Providing temporary use of non-downloadable computer software for electronic data capture and data management in the fields of oncology and customized genetic research; Providing medical and scientific research information in the fields of oncology and customized genetic research.

Produits et services

Medical research; Medical and scientific research in the field of genomics; genomic sequencing and genomic analysis Medical services; Genetic and health information analysis and diagnostics for medical purposes

Produits et services

(1) Downloadable software in the nature of a mobile application using artificial intelligence (AI) for use in providing personal health care and mental health support services to patients, namely, assisting patients with tracking symptoms and health metrics, providing access to medical records and health information, communicating with healthcare providers, preparing for medical appointments, and providing personalized health insights to individual patients. (1) Software as a service (SAAS) featuring online non-downloadable software for use in providing recommendations for patient treatment and care in the field of mental health; software as a service (SAAS) featuring artificial intelligence for machine learning and predictive analytics to support clinical data platforms for patient diagnosis and treatment in the field of mental health. (2) Medical services; providing medical information and patient health information; providing medical information to patients using artificial intelligence to provide personalized health insights for individual patients.

Abrégé

An artificial intelligence engine for directed hypothesis generation and ranking uses multiple heterogeneous knowledge graphs integrating disease-specific multi-omic data specific to a patient or cohort of patients. The engine also uses a knowledge graph representation of ‘what the world knows’ in the relevant bio-medical subspace. The engine applies a hypothesis generation module, a semantic search analysis component to allow fast acquiring and construction of cohorts, as well as aggregating, summarizing, visualizing and returning ranked multi-omic alterations in terms of clinical actionability and degree of surprise for individual samples and cohorts. The engine also applies a moderator module that ranks and filters hypotheses, where the most promising hypothesis can be presented to domain experts (e.g., physicians, oncologists, pathologists, radiologists and researchers) for feedback. The engine also uses a continuous integration module that iteratively refines and updates entities and relationships and their representations to yield higher quality of hypothesis generation over time.

Classes IPC  ?

• G06N 5/04 - Modèles d’inférence ou de raisonnement
• G06F 16/2457 - Traitement des requêtes avec adaptation aux besoins de l’utilisateur
• G06F 16/28 - Bases de données caractérisées par leurs modèles, p. ex. des modèles relationnels ou objet
• G06N 3/045 - Combinaisons de réseaux
• G06N 5/02 - Représentation de la connaissanceReprésentation symbolique

Abrégé

Systems and methods for identifying a model to perform a task are provided. Each model in a plurality of models is inputted with information for each sample in a plurality of samples. Subsets of samples correspond to labels. Spectra are obtained from outputs of layers in the models by applying parameters against the information. The spectra are dimension reduced to obtain component value sets that correspond to samples and collectively have an explained variance of at least a threshold amount of the total variance. For each model, a divergence is determined using a mathematical combination of a plurality of distances, where each distance is between each label subset of samples relative to all other samples. A model having a divergence satisfying a threshold is identified to perform the task. Systems and methods for updating the architecture of a model to perform a task are also provided.

Classes IPC  ?

• G06N 20/00 - Apprentissage automatique

Abrégé

Systems, methods, and apparatuses for imputing a value associated with a subject within an electronic health record (EHR) system are disclosed herein. A request to impute a value associated with the subject at a diagnostic temporal instance is received, and a subset of data associated with the subject from an EHR system is retrieved. One or more temporal windows are determined and used to select health observations having a temporal instance within the one or more temporal windows. Values corresponding to the selected health observations are retrieved as input values, which are provided as input to a trained artificial intelligence engine. The trained artificial intelligence engine processes the input values to generate the imputed value and provides the imputed value in response to the request.

Classes IPC  ?

• G16H 10/60 - TIC spécialement adaptées au maniement ou au traitement des données médicales ou de soins de santé relatives aux patients pour des données spécifiques de patients, p. ex. pour des dossiers électroniques de patients
• G16H 10/20 - TIC spécialement adaptées au maniement ou au traitement des données médicales ou de soins de santé relatives aux patients pour des essais ou des questionnaires cliniques électroniques
• G16H 50/20 - TIC spécialement adaptées au diagnostic médical, à la simulation médicale ou à l’extraction de données médicalesTIC spécialement adaptées à la détection, au suivi ou à la modélisation d’épidémies ou de pandémies pour le diagnostic assisté par ordinateur, p. ex. basé sur des systèmes experts médicaux

Abrégé

A method of identifying a variant as a somatic variant derived from cell free DNA (cfDNA) identifies the variant at a locus based on differences between the nucleic acid sequence for a cfDNA fragment in a plurality of cfDNA fragments and a nucleic acid sequence for the locus in a reference sequence, where the cfDNA fragments are from a liquid biopsy sample from a subject. A set of cfDNA fragments comprising the variant, in the plurality of cfDNA fragments, determines fragment length metrics. A variant allele fraction (VAF) is determined based on comparison of the number of cfDNA fragments having the variant and the total number of cfDNA fragments mapping to the locus. Clonal hematopoiesis prevalence metrics for the variant are obtained. The fragment length metrics, VAF, and hematopoiesis metrics are inputted into a model thereby obtaining, as model output, whether the variant is a somatic variant derived from cfDNA.

Classes IPC  ?

• G16B 20/20 - Détection d’allèles ou de variantes, p. ex. détection de polymorphisme d’un seul nucléotide
• G16B 30/10 - Alignement de séquenceRecherche d’homologie
• G16B 40/20 - Analyse de données supervisée

Abrégé

A method of identifying a variant as a somatic variant derived from cell free DNA (cfDNA) identifies the variant at a locus based on differences between the nucleic acid sequence for a cfDNA fragment in a plurality of cfDNA fragments and a nucleic acid sequence for the locus in a reference sequence, where the cfDNA fragments are from a liquid biopsy sample from a subject. A set of cfDNA fragments comprising the variant, in the plurality of cfDNA fragments, determines fragment length metrics. A variant allele fraction (VAF) is determined based on comparison of the number of cfDNA fragments having the variant and the total number of cfDNA fragments mapping to the locus. Clonal hematopoiesis prevalence metrics for the variant are obtained. The fragment length metrics, VAF, and hematopoiesis metrics are inputted into a model thereby obtaining, as model output, whether the variant is a somatic variant derived from cfDNA.

Classes IPC  ?

• C12Q 1/6886 - Produits d’acides nucléiques utilisés dans l’analyse d’acides nucléiques, p. ex. amorces ou sondes pour les maladies provoquées par des altérations du matériel génétique pour le cancer
• G16B 20/00 - TIC spécialement adaptées à la génomique ou protéomique fonctionnelle, p. ex. corrélations génotype-phénotype
• G16B 40/20 - Analyse de données supervisée

Produits et services

Providing temporary use of non-downloadable computer software for analyzing health information in support of making medical decisions in the medical field; providing medical and scientific research information in the fields of oncology and customized genetic research; providing temporary use of non-downloadable computer software for analyzing and predicting nucleic acid yield. Medical services; providing medical information in the field of oncology; medical services in the field of oncology diagnostics and testing; providing medical diagnostic testing that analyzes patient samples and predicts nucleic acid yield.

Abrégé

A computer-implemented method, computing system and computer-readable medium for determining HER2-low status of a patient using molecular data of the patient includes: (a) receiving digital biological data; (b) processing the digital biological data using a trained multi-stage machine learning architecture; (c) generating a digital HER2-low status report corresponding to the patient; and (d) causing the digital HER2-low status report to be displayed. A computer-implemented method, computing system and computer-readable medium for training a model architecture to determine HER2-low status of a patient using molecular data of the patient includes: (a) receiving training digital biological data; (b) initializing a machine learning model; (c) processing the plurality of molecular signatures using the machine learning model to generate a trained machine learning model; and (d) storing the trained machine learning.

Classes IPC  ?

• G16B 40/20 - Analyse de données supervisée
• A61B 10/00 - Instruments pour le prélèvement d'échantillons corporels à des fins de diagnostic Autres procédés ou instruments pour le diagnostic, p. ex. pour le diagnostic de vaccination ou la détermination du sexe ou de la période d'ovulationInstruments pour gratter la gorge
• G16H 10/40 - TIC spécialement adaptées au maniement ou au traitement des données médicales ou de soins de santé relatives aux patients pour des données relatives aux analyses de laboratoire, p. ex. pour des analyses d’échantillon de patient
• G16H 15/00 - TIC spécialement adaptées aux rapports médicaux, p. ex. leur création ou leur transmission
• G16H 20/10 - TIC spécialement adaptées aux thérapies ou aux plans d’amélioration de la santé, p. ex. pour manier les prescriptions, orienter la thérapie ou surveiller l’observance par les patients concernant des médicaments ou des médications, p. ex. pour s’assurer de l’administration correcte aux patients

Abrégé

A implemented method, computing system and computer-readable medium for stratifying patient cancer risk using molecular data includes receiving molecular data; processing the molecular data using a machine learning model; and generating a matched treatment strategy for the patient based upon the patient's molecular data risk. A computer-implemented method, computing system and computer-readable medium for training a machine learning model to stratify patient cancer risk using molecular data includes receiving a patient training dataset, and a reference training dataset; selecting a cohort of patients; selecting a small subset of genes using univariate selection; generating a corrected reference training dataset; selecting a smaller subset of genes using multivariate selection; training a survival model; and (g) selecting a decision threshold to identify a patient population.

Classes IPC  ?

• G16H 50/30 - TIC spécialement adaptées au diagnostic médical, à la simulation médicale ou à l’extraction de données médicalesTIC spécialement adaptées à la détection, au suivi ou à la modélisation d’épidémies ou de pandémies pour le calcul des indices de santéTIC spécialement adaptées au diagnostic médical, à la simulation médicale ou à l’extraction de données médicalesTIC spécialement adaptées à la détection, au suivi ou à la modélisation d’épidémies ou de pandémies pour l’évaluation des risques pour la santé d’une personne
• G16H 10/60 - TIC spécialement adaptées au maniement ou au traitement des données médicales ou de soins de santé relatives aux patients pour des données spécifiques de patients, p. ex. pour des dossiers électroniques de patients

Abrégé

Methods, systems, and software are provided for estimating a circulating tumor fraction (ctFE) for a test subject. Sequence reads are obtained from a panel-enriched sequencing reaction, including sequences for cfDNA fragments corresponding to probe sequences and sequences for cfDNA fragments not corresponding to probe sequences. Bin coverage values are determined from the sequences. Segments are formed by grouping adjacent bins based on similar coverage value and segment coverage values are determined based on bin coverage values for bins mapping to each segment. For each simulated ctFE in a plurality of ctFEs, segments are fitted to an integer copy state by identifying the integer copy state that best matches the segment coverage value. The circulating tumor fraction for the test subject is determined using error optimization between segment coverage values and integer copy states across the simulated ctFEs.

Classes IPC  ?

• G16B 20/10 - Ploïdie ou détection du nombre de copies
• G16B 20/20 - Détection d’allèles ou de variantes, p. ex. détection de polymorphisme d’un seul nucléotide
• G16B 40/20 - Analyse de données supervisée

Abrégé

Disclosed herein are systems, methods, and compositions for identifying subjects likely to respond to an immune oncology therapy. The disclosed methods may include applying one or more model components to a machine learning algorithm. The one or more model components are derived from RNA and/or DNA sequencing from a subject and may include a checkpoint related gene signature, an immune exhaustion signature, a immune oncology signature, a tumor mutational burden, or a granulocytic myeloid derived suppressor cell signature.

Classes IPC  ?

• G16H 50/20 - TIC spécialement adaptées au diagnostic médical, à la simulation médicale ou à l’extraction de données médicalesTIC spécialement adaptées à la détection, au suivi ou à la modélisation d’épidémies ou de pandémies pour le diagnostic assisté par ordinateur, p. ex. basé sur des systèmes experts médicaux
• G16B 20/00 - TIC spécialement adaptées à la génomique ou protéomique fonctionnelle, p. ex. corrélations génotype-phénotype
• G16B 30/00 - TIC spécialement adaptées à l’analyse de séquences impliquant des nucléotides ou des aminoacides
• G16B 40/20 - Analyse de données supervisée
• G16H 20/10 - TIC spécialement adaptées aux thérapies ou aux plans d’amélioration de la santé, p. ex. pour manier les prescriptions, orienter la thérapie ou surveiller l’observance par les patients concernant des médicaments ou des médications, p. ex. pour s’assurer de l’administration correcte aux patients

Abrégé

TCR identification methods that find particular use in the development of TCR-based cancer therapies are described. The subject TCR screening methods utilize a comprehensive cancer subject database to effectively identify candidate TCRs that recognize a peptide of interest (e.g., a neoantigen) in complex with an HLA allele.

Classes IPC  ?

• G16B 20/30 - Détection de sites de liaison ou de motifs
• G01N 33/68 - Analyse chimique de matériau biologique, p. ex. de sang ou d'urineTest par des méthodes faisant intervenir la formation de liaisons biospécifiques par ligandsTest immunologique faisant intervenir des protéines, peptides ou amino-acides

Abrégé

Methods, systems, and software are provided for estimating a circulating tumor fraction (ctFE) for a test subject. Sequence reads are obtained from a panel-enriched sequencing reaction, including sequences for cfDNA fragments corresponding to probe sequences and sequences for cfDNA fragments not corresponding to probe sequences. Bin coverage values are determined from the sequences. Segments are formed by grouping adjacent bins based on similar coverage value and segment coverage values are determined based on bin coverage values for bins mapping to each segment. For each simulated ctFE in a plurality of ctFEs, segments are fitted to an integer copy state by identifying the integer copy state that best matches the segment coverage value. The circulating tumor fraction for the test subject is determined using error optimization between segment coverage values and integer copy states across the simulated ctFEs.

Classes IPC  ?

• C12Q 1/6886 - Produits d’acides nucléiques utilisés dans l’analyse d’acides nucléiques, p. ex. amorces ou sondes pour les maladies provoquées par des altérations du matériel génétique pour le cancer
• C12Q 1/6874 - Méthodes de séquençage faisant intervenir des réseaux d’acides nucléiques, p. ex. séquençage par hybridation [SBH]
• G16B 30/00 - TIC spécialement adaptées à l’analyse de séquences impliquant des nucléotides ou des aminoacides

Abrégé

A method and system of audibly broadcasting responses to a user based on user queries about a specific patient report, the method comprising receiving an audible query from the user to a microphone coupled to a collaboration device, identifying at least one intent associated with the audible query, identifying at least one data operation associated with the at least one intent, associating each of the at least one data operations with a first set of data presented on the report, executing each of the at least one data operations on a second set of data to generate response data, generating an audible response file associated with the response data and providing the audible response file for broadcasting via a speaker coupled to the collaboration device.

Classes IPC  ?

• G10L 15/22 - Procédures utilisées pendant le processus de reconnaissance de la parole, p. ex. dialogue homme-machine
• G06F 3/0481 - Techniques d’interaction fondées sur les interfaces utilisateur graphiques [GUI] fondées sur des propriétés spécifiques de l’objet d’interaction affiché ou sur un environnement basé sur les métaphores, p. ex. interaction avec des éléments du bureau telles les fenêtres ou les icônes, ou avec l’aide d’un curseur changeant de comportement ou d’aspect
• G06F 3/16 - Entrée acoustiqueSortie acoustique
• G06F 16/632 - Formulation de requêtes
• G16H 10/60 - TIC spécialement adaptées au maniement ou au traitement des données médicales ou de soins de santé relatives aux patients pour des données spécifiques de patients, p. ex. pour des dossiers électroniques de patients
• G16H 40/20 - TIC spécialement adaptées à la gestion ou à l’administration de ressources ou d’établissements de santéTIC spécialement adaptées à la gestion ou au fonctionnement d’équipement ou de dispositifs médicaux pour la gestion ou l’administration de ressources ou d’établissements de soins de santé, p. ex. pour la gestion du personnel hospitalier ou de salles d’opération
• G16H 50/20 - TIC spécialement adaptées au diagnostic médical, à la simulation médicale ou à l’extraction de données médicalesTIC spécialement adaptées à la détection, au suivi ou à la modélisation d’épidémies ou de pandémies pour le diagnostic assisté par ordinateur, p. ex. basé sur des systèmes experts médicaux
• G16H 50/50 - TIC spécialement adaptées au diagnostic médical, à la simulation médicale ou à l’extraction de données médicalesTIC spécialement adaptées à la détection, au suivi ou à la modélisation d’épidémies ou de pandémies pour la simulation ou la modélisation des troubles médicaux
• G16H 70/20 - TIC spécialement adaptées au maniement ou au traitement de références médicales concernant des pratiques ou des directives
• G16H 70/60 - TIC spécialement adaptées au maniement ou au traitement de références médicales concernant des pathologies
• H04B 1/3827 - Émetteurs-récepteurs portatifs

Abrégé

A computer program product includes multiple microservices for interrogating clinical records according to one or more projects associated with patient datasets obtained from electronic copies of source documents from the clinical records. A first microservice generates a user interface including a first portion displaying source documents and, concurrently, a second portion displaying structured patient data fields organized into categories for entering structured patient data derived from the source documents displayed in the first portion. Categories and their organization are defined by a template and include cancer diagnosis, staging, tumor size, genetic results, and date of recurrence. A second microservice validates abstracted patient data according to validation rules applied to the categories, validation rules being assigned to the projects and performed on the categories as they are populated. A third microservice provides abstraction review performed by an assigned abstractor or an abstraction manager and spans one or more of the projects.

Classes IPC  ?

• G16H 10/60 - TIC spécialement adaptées au maniement ou au traitement des données médicales ou de soins de santé relatives aux patients pour des données spécifiques de patients, p. ex. pour des dossiers électroniques de patients
• G06F 40/186 - Gabarits
• G06Q 50/22 - Aide sociale ou assistance sociale, p. ex. activités de développement communautaire ou services de consultation
• G16H 50/20 - TIC spécialement adaptées au diagnostic médical, à la simulation médicale ou à l’extraction de données médicalesTIC spécialement adaptées à la détection, au suivi ou à la modélisation d’épidémies ou de pandémies pour le diagnostic assisté par ordinateur, p. ex. basé sur des systèmes experts médicaux

Abrégé

A method for qualifying a specimen prepared on one or more hematoxylin and eosin (H&E) slides by assessing an expected yield of nucleic acids for tumor cells and providing associated unstained slides for subsequent nucleic acid analysis is provided.

Classes IPC  ?

• G06T 1/20 - Architectures de processeursConfiguration de processeurs p. ex. configuration en pipeline
• C12Q 1/6869 - Méthodes de séquençage
• G06V 10/25 - Détermination d’une région d’intérêt [ROI] ou d’un volume d’intérêt [VOI]
• G06V 10/44 - Extraction de caractéristiques locales par analyse des parties du motif, p. ex. par détection d’arêtes, de contours, de boucles, d’angles, de barres ou d’intersectionsAnalyse de connectivité, p. ex. de composantes connectées
• G06V 10/764 - Dispositions pour la reconnaissance ou la compréhension d’images ou de vidéos utilisant la reconnaissance de formes ou l’apprentissage automatique utilisant la classification, p. ex. des objets vidéo
• G06V 10/774 - Génération d'ensembles de motifs de formationTraitement des caractéristiques d’images ou de vidéos dans les espaces de caractéristiquesDispositions pour la reconnaissance ou la compréhension d’images ou de vidéos utilisant la reconnaissance de formes ou l’apprentissage automatique utilisant l’intégration et la réduction de données, p. ex. analyse en composantes principales [PCA] ou analyse en composantes indépendantes [ ICA] ou cartes auto-organisatrices [SOM]Séparation aveugle de source méthodes de Bootstrap, p. ex. "bagging” ou “boosting”
• G06V 10/82 - Dispositions pour la reconnaissance ou la compréhension d’images ou de vidéos utilisant la reconnaissance de formes ou l’apprentissage automatique utilisant les réseaux neuronaux
• G06V 20/69 - Objets microscopiques, p. ex. cellules biologiques ou pièces cellulaires
• G16B 30/00 - TIC spécialement adaptées à l’analyse de séquences impliquant des nucléotides ou des aminoacides
• G16B 50/30 - Entreposage de donnéesArchitectures informatiques

Abrégé

Systems and methods for molecular residual disease (MRD) status determination for a subject's cancer condition subject uses sequence reads from methylation sequencing of cell-free DNA fragments from the subject's liquid biopsy sample to determine their methylation patterns. From this, corresponding numbers of circulating-tumor DNA (ctDNA) fragments mapping to each of a plurality of regions of the genome of the subject's species are determined. Corresponding expected numbers of noise fragments in each region are determined based on corresponding distributions using observed sequencing depths and learned background emission rates for each region. An excess fragments per million (FPM) value, corrected by an observed CHG methylation level, is determined from the observed number of ctDNA fragments in excess of the expected number of noise fragments in each region. MRD is called when the CHG corrected excess FPM value satisfies a threshold value, and a call against MRD is made when it does not.

Classes IPC  ?

• C12Q 1/6886 - Produits d’acides nucléiques utilisés dans l’analyse d’acides nucléiques, p. ex. amorces ou sondes pour les maladies provoquées par des altérations du matériel génétique pour le cancer
• G16B 20/20 - Détection d’allèles ou de variantes, p. ex. détection de polymorphisme d’un seul nucléotide
• G16B 40/20 - Analyse de données supervisée
• G16H 50/20 - TIC spécialement adaptées au diagnostic médical, à la simulation médicale ou à l’extraction de données médicalesTIC spécialement adaptées à la détection, au suivi ou à la modélisation d’épidémies ou de pandémies pour le diagnostic assisté par ordinateur, p. ex. basé sur des systèmes experts médicaux

Abrégé

Systems and methods for molecular residual disease (MRD) status determination for a subject's cancer condition subject uses sequence reads from methylation sequencing of cell-free DNA fragments from the subject's liquid biopsy sample to determine their methylation patterns. From this, corresponding numbers of circulating-tumor DNA (ctDNA) fragments mapping to each of a plurality of regions of the genome of the subject's species are determined. Corresponding expected numbers of noise fragments in each region are determined based on corresponding distributions using observed sequencing depths and learned background emission rates for each region. An excess fragments per million (FPM) value, corrected by an observed CHG methylation level, is determined from the observed number of ctDNA fragments in excess of the expected number of noise fragments in each region. MRD is called when the CHG corrected excess FPM value satisfies a threshold value, and a call against MRD is made when it does not.

Classes IPC  ?

• G16H 50/20 - TIC spécialement adaptées au diagnostic médical, à la simulation médicale ou à l’extraction de données médicalesTIC spécialement adaptées à la détection, au suivi ou à la modélisation d’épidémies ou de pandémies pour le diagnostic assisté par ordinateur, p. ex. basé sur des systèmes experts médicaux
• G16B 20/10 - Ploïdie ou détection du nombre de copies
• G16B 20/20 - Détection d’allèles ou de variantes, p. ex. détection de polymorphisme d’un seul nucléotide
• G16H 20/10 - TIC spécialement adaptées aux thérapies ou aux plans d’amélioration de la santé, p. ex. pour manier les prescriptions, orienter la thérapie ou surveiller l’observance par les patients concernant des médicaments ou des médications, p. ex. pour s’assurer de l’administration correcte aux patients

Abrégé

This present disclosure relates to systems, methods, and compositions useful for profiling T cell receptor (TCR) and B cell receptor (BCR) repertoire using next-generation sequencing (NGS) methods. The present disclosure also relates to systems and methods for diagnosing, treating, or predicting infection, disease, medical conditions, therapeutic outcome, or therapeutic efficacy based on the TCR/BCR profile data from a subject in need thereof.

Classes IPC  ?

• C12Q 1/6881 - Produits d’acides nucléiques utilisés dans l’analyse d’acides nucléiques, p. ex. amorces ou sondes pour le typage de tissu ou de cellule, p. ex. sondes d’antigène leucocytaire humain [HLA]
• C12Q 1/6806 - Préparation d’acides nucléiques pour analyse, p. ex. pour test de réaction en chaîne par polymérase [PCR]
• C12Q 1/6809 - Méthodes de détermination ou d’identification des acides nucléiques faisant intervenir la détection différentielle
• C12Q 1/6874 - Méthodes de séquençage faisant intervenir des réseaux d’acides nucléiques, p. ex. séquençage par hybridation [SBH]
• C12Q 1/6883 - Produits d’acides nucléiques utilisés dans l’analyse d’acides nucléiques, p. ex. amorces ou sondes pour les maladies provoquées par des altérations du matériel génétique

Produits et services

Downloadable software in the nature of a mobile application using artificial intelligence (AI) for use in providing personal health care support services to patients, assisting patients with tracking health metrics and data, providing access to medical records and health information, communicating with healthcare providers, preparing for medical appointments, and providing personalized health insights to patients. Medical services; providing medical information and patient health information; providing medical information to patients using artificial intelligence to provide personalized health insights for patients.

Produits et services

(1) Downloadable software in the nature of a mobile application using artificial intelligence (AI) for use in providing personal health care support services to patients, assisting patients with tracking health metrics and data, providing access to medical records and health information, communicating with healthcare providers, preparing for medical appointments, and providing personalized health insights to patients. (1) Medical services; providing medical information and patient health information; providing medical information to patients using artificial intelligence to provide personalized health insights for patients.

Produits et services

Computerized database management and data processing services in the fields of oncology and customized genetic research Providing temporary use of non-downloadable computer software for electronic data capture and data management in the fields of oncology and customized genetic research; Providing medical and scientific research information in the fields of oncology and customized genetic research

Produits et services

Downloadable software in the nature of a mobile application using artificial intelligence (AI) for use in providing personal health care and mental health support services to patients, namely, assisting patients with tracking symptoms and health metrics, providing access to medical records and health information, communicating with healthcare providers, preparing for medical appointments, and providing personalized health insights to individual patients Software as a service (SAAS) featuring online non-downloadable software for use in providing recommendations for patient treatment and care in the field of mental health; software as a service (SAAS) featuring artificial intelligence for machine learning and predictive analytics to support clinical data platforms for patient diagnosis and treatment in the field of mental health Medical services; providing medical information and patient health information; providing medical information to patients using artificial intelligence to provide personalized health insights for individual patients.

Abrégé

A method, computing system, and computer-readable medium may receive and process de novo quantitative measures input using a trained machine learning model to generate one or more predicted experimental cell quantitative measures. A method, computing system, and computer-readable medium may receive training quantitative measures data; train a machine learning model to generate predicted experimental cell viability measures based on a de novo quantitative measures input, by processing the training quantitative measures data; and store the machine learning model as a trained machine learning model in a memory of a computer.

Classes IPC  ?

• G16H 10/40 - TIC spécialement adaptées au maniement ou au traitement des données médicales ou de soins de santé relatives aux patients pour des données relatives aux analyses de laboratoire, p. ex. pour des analyses d’échantillon de patient
• G06T 3/4046 - Changement d'échelle d’images complètes ou de parties d’image, p. ex. agrandissement ou rétrécissement utilisant des réseaux neuronaux
• G16H 30/40 - TIC spécialement adaptées au maniement ou au traitement d’images médicales pour le traitement d’images médicales, p. ex. l’édition

Abrégé

The present disclosure relates to predicting a data element in an electronic health record (EHR) for a subject using a trained machine learning model including an attention module. An example method includes obtaining a query for the prediction of the data element, obtaining a plurality of observations about the subject, processing the query and observations with the trained machine learning model having an attention module to generate a prediction of the subject characteristic, and providing the prediction of the data element as an output.

Classes IPC  ?

• G16H 10/60 - TIC spécialement adaptées au maniement ou au traitement des données médicales ou de soins de santé relatives aux patients pour des données spécifiques de patients, p. ex. pour des dossiers électroniques de patients

Abrégé

The present disclosure relates to predicting a data element in an electronic health record (EHR) for a subject using a trained machine learning model including an attention module. An example method includes obtaining a query for the prediction of the data element, obtaining a plurality of observations about the subject, processing the query and observations with the trained machine learning model having an attention module to generate a prediction of the subject characteristic, and providing the prediction of the data element as an output.

Classes IPC  ?

• G16H 15/00 - TIC spécialement adaptées aux rapports médicaux, p. ex. leur création ou leur transmission
• G16H 10/60 - TIC spécialement adaptées au maniement ou au traitement des données médicales ou de soins de santé relatives aux patients pour des données spécifiques de patients, p. ex. pour des dossiers électroniques de patients
• G16H 10/20 - TIC spécialement adaptées au maniement ou au traitement des données médicales ou de soins de santé relatives aux patients pour des essais ou des questionnaires cliniques électroniques
• G16H 20/00 - TIC spécialement adaptées aux thérapies ou aux plans d’amélioration de la santé, p. ex. pour manier les prescriptions, orienter la thérapie ou surveiller l’observance par les patients
• G16H 20/10 - TIC spécialement adaptées aux thérapies ou aux plans d’amélioration de la santé, p. ex. pour manier les prescriptions, orienter la thérapie ou surveiller l’observance par les patients concernant des médicaments ou des médications, p. ex. pour s’assurer de l’administration correcte aux patients
• G16H 50/20 - TIC spécialement adaptées au diagnostic médical, à la simulation médicale ou à l’extraction de données médicalesTIC spécialement adaptées à la détection, au suivi ou à la modélisation d’épidémies ou de pandémies pour le diagnostic assisté par ordinateur, p. ex. basé sur des systèmes experts médicaux
• G16H 50/30 - TIC spécialement adaptées au diagnostic médical, à la simulation médicale ou à l’extraction de données médicalesTIC spécialement adaptées à la détection, au suivi ou à la modélisation d’épidémies ou de pandémies pour le calcul des indices de santéTIC spécialement adaptées au diagnostic médical, à la simulation médicale ou à l’extraction de données médicalesTIC spécialement adaptées à la détection, au suivi ou à la modélisation d’épidémies ou de pandémies pour l’évaluation des risques pour la santé d’une personne
• G06F 7/00 - Procédés ou dispositions pour le traitement de données en agissant sur l'ordre ou le contenu des données maniées
• G06N 3/02 - Réseaux neuronaux
• G06N 3/08 - Méthodes d'apprentissage
• G06N 3/045 - Combinaisons de réseaux
• G16H 50/70 - TIC spécialement adaptées au diagnostic médical, à la simulation médicale ou à l’extraction de données médicalesTIC spécialement adaptées à la détection, au suivi ou à la modélisation d’épidémies ou de pandémies pour extraire des données médicales, p. ex. pour analyser les cas antérieurs d’autres patients
• G06F 5/00 - Procédés ou dispositions pour la conversion de données, sans modification de l'ordre ou du contenu des données maniées

Abrégé

In the disclosed systems and methods for characterizing a cancer condition of a tissue in a subject, a computer system inputs information into an ensemble model. The information includes, for each respective class of radiomics features in a plurality of classes of radiomics features, a corresponding value for each respective radiomic feature in a corresponding plurality of radiomics features of the respective class of radiomics features obtained from a medical imaging dataset. The ensemble model comprises a plurality of component models. The computer system obtains as output from each respective component model in the plurality of component models a corresponding component prediction for the cancer condition, thereby obtaining a plurality of component predictions for the cancer condition. The computer system combines the plurality of component predictions to obtain as output of the ensemble model a characterization of the cancer condition.

Classes IPC  ?

• G16H 50/20 - TIC spécialement adaptées au diagnostic médical, à la simulation médicale ou à l’extraction de données médicalesTIC spécialement adaptées à la détection, au suivi ou à la modélisation d’épidémies ou de pandémies pour le diagnostic assisté par ordinateur, p. ex. basé sur des systèmes experts médicaux

Abrégé

Methods, systems, and software are provided for monitoring a cancer condition of a test subject. The method includes obtaining a liquid biopsy sample from the subject at a second time point, occurring after a first time point, containing cell-free DNA fragments. Low-pass whole genome methylation sequencing of the cell-free DNA fragments is performed to obtain nucleic acid sequences having a methylation pattern for a corresponding cell-free DNA fragment. The nucleic acid sequences are mapped to a location on a reference genome. Methylation metrics are determined based on the methylation patterns and mapped locations of the nucleic acid sequences. A circulating tumor fraction is estimated from the methylation metrics, and the estimate is compared to an estimate of the circulating tumor fraction for the test subject at the first time point.

Classes IPC  ?

• G16B 40/00 - TIC spécialement adaptées aux biostatistiquesTIC spécialement adaptées à l’apprentissage automatique ou à l’exploration de données liées à la bio-informatique, p. ex. extraction de connaissances ou détection de motifs
• G06F 30/27 - Optimisation, vérification ou simulation de l’objet conçu utilisant l’apprentissage automatique, p. ex. l’intelligence artificielle, les réseaux neuronaux, les machines à support de vecteur [MSV] ou l’apprentissage d’un modèle
• G06F 111/08 - CAO probabiliste ou stochastique
• G16B 5/20 - Modèles probabilistes
• G16B 20/20 - Détection d’allèles ou de variantes, p. ex. détection de polymorphisme d’un seul nucléotide
• G16B 30/00 - TIC spécialement adaptées à l’analyse de séquences impliquant des nucléotides ou des aminoacides
• G16B 40/20 - Analyse de données supervisée

Abrégé

The application includes systems and methods for phenotyping. A request is received to identify a target population having a phenotype. Predefined characteristics associated with the phenotype are obtained. Using a retriever model, a set of subjects is identified as potential members of the target population by searching databases. Medical information is obtained by searching a second set of one or more databases and providing corresponding information to an artificial intelligence (AI) component. Natural language instructions are provided to the AI component to provide context to the AI component to determine whether a respective subject in the set of subjects has at least one of the one or more predefined characteristics. A subset of subjects is identified by the AI system to be, or to have a high likelihood of being, a member of the target population.

Classes IPC  ?

• G16H 10/60 - TIC spécialement adaptées au maniement ou au traitement des données médicales ou de soins de santé relatives aux patients pour des données spécifiques de patients, p. ex. pour des dossiers électroniques de patients

Abrégé

In the disclosed systems and methods for categorizing medical data, a computer system obtains, in electronic form, a plurality of medical records. Each medical record includes corresponding medical data from a respective medical evaluation and corresponding metadata comprising a plurality of attributes about the respective medical evaluation. Each respective attribute comprises a corresponding string of text. The computer system determines, for each respective pair of medical records consisting of a first medical record and a second medical record, a corresponding pairwise similarity between, for each respective attribute in a set of attributes, the corresponding string of text for the first medical record and the corresponding string of text for the second medical record. The computer system identifies a first subset of the plurality of medical records. Each respective medical record in the first subset is connected to each other through pairwise similarities that each satisfies a similarity threshold.

Classes IPC  ?

• G06V 10/774 - Génération d'ensembles de motifs de formationTraitement des caractéristiques d’images ou de vidéos dans les espaces de caractéristiquesDispositions pour la reconnaissance ou la compréhension d’images ou de vidéos utilisant la reconnaissance de formes ou l’apprentissage automatique utilisant l’intégration et la réduction de données, p. ex. analyse en composantes principales [PCA] ou analyse en composantes indépendantes [ ICA] ou cartes auto-organisatrices [SOM]Séparation aveugle de source méthodes de Bootstrap, p. ex. "bagging” ou “boosting”
• G06T 7/00 - Analyse d'image
• G06V 10/74 - Appariement de motifs d’image ou de vidéoMesures de proximité dans les espaces de caractéristiques

Abrégé

The application includes systems and methods for phenotyping. A request is received to identify a target population having a phenotype. Predefined characteristics associated with the phenotype are obtained. Using a retriever model, a set of subjects is identified as potential members of the target population by searching databases. Medical information is obtained by searching a second set of one or more databases and providing corresponding information to an artificial intelligence (Al) component. Natural language instructions are provided to the Al component to provide context to the Al component to determine whether a respective subject in the set of subjects has at least one of the one or more predefined characteristics. A subset of subjects is identified by the Al system to be, or to have a high likelihood of being, a member of the target population.

Classes IPC  ?

• G16H 50/70 - TIC spécialement adaptées au diagnostic médical, à la simulation médicale ou à l’extraction de données médicalesTIC spécialement adaptées à la détection, au suivi ou à la modélisation d’épidémies ou de pandémies pour extraire des données médicales, p. ex. pour analyser les cas antérieurs d’autres patients
• G16H 20/10 - TIC spécialement adaptées aux thérapies ou aux plans d’amélioration de la santé, p. ex. pour manier les prescriptions, orienter la thérapie ou surveiller l’observance par les patients concernant des médicaments ou des médications, p. ex. pour s’assurer de l’administration correcte aux patients
• G16H 50/20 - TIC spécialement adaptées au diagnostic médical, à la simulation médicale ou à l’extraction de données médicalesTIC spécialement adaptées à la détection, au suivi ou à la modélisation d’épidémies ou de pandémies pour le diagnostic assisté par ordinateur, p. ex. basé sur des systèmes experts médicaux
• G16H 70/60 - TIC spécialement adaptées au maniement ou au traitement de références médicales concernant des pathologies

Abrégé

An evaluation of a cancer treatment start date for a cancer medication identifies a first plurality of subjects and, for each, the treatment start date. A subject is selected for a second plurality of subjects by applying features for the subject to a model at a propensity value threshold. One subset of the features is associated with a time period and another subset is static. The applying obtains anchor point predictions, each associated with a time in the time period and including a probability that the time is the start date. The time of the anchor point prediction having the greatest probability is assigned the anchor point of the subject. The start date is evaluated with a survival objective based on the start date for each subject in the first plurality and the assigned anchor point for each subject in the second plurality.

Classes IPC  ?

• G16H 50/30 - TIC spécialement adaptées au diagnostic médical, à la simulation médicale ou à l’extraction de données médicalesTIC spécialement adaptées à la détection, au suivi ou à la modélisation d’épidémies ou de pandémies pour le calcul des indices de santéTIC spécialement adaptées au diagnostic médical, à la simulation médicale ou à l’extraction de données médicalesTIC spécialement adaptées à la détection, au suivi ou à la modélisation d’épidémies ou de pandémies pour l’évaluation des risques pour la santé d’une personne
• A61B 5/00 - Mesure servant à établir un diagnostic Identification des individus
• G06F 3/01 - Dispositions d'entrée ou dispositions d'entrée et de sortie combinées pour l'interaction entre l'utilisateur et le calculateur
• G16H 20/40 - TIC spécialement adaptées aux thérapies ou aux plans d’amélioration de la santé, p. ex. pour manier les prescriptions, orienter la thérapie ou surveiller l’observance par les patients concernant des thérapies mécaniques, la radiothérapie ou des thérapies invasives, p. ex. la chirurgie, la thérapie laser, la dialyse ou l’acuponcture
• G16H 50/70 - TIC spécialement adaptées au diagnostic médical, à la simulation médicale ou à l’extraction de données médicalesTIC spécialement adaptées à la détection, au suivi ou à la modélisation d’épidémies ou de pandémies pour extraire des données médicales, p. ex. pour analyser les cas antérieurs d’autres patients

Abrégé

This application describes, among other things, systems and methods for interacting with a task-specific orchestration. An example method includes obtaining medical data from one or more data collections. A set of user interface elements is presented at a user interface, where each respective user interface element of the set of user interface elements represents a respective task-specific orchestration that includes one or more respective machine-learning models fine-tuned for a specific task or domain based on the medical data. When a user interface element representing a respective task-specific orchestration is selected, at least some of the medical data is provided to the respective task-specific orchestration, and a different user interface is presented for communicating with the respective task-specific orchestration. Based on receiving a prompt at the different user interface, a response object is generated by the task-specific orchestration based on the prompt and at least some of the medical data.

Classes IPC  ?

• G16H 10/20 - TIC spécialement adaptées au maniement ou au traitement des données médicales ou de soins de santé relatives aux patients pour des essais ou des questionnaires cliniques électroniques
• G16H 10/60 - TIC spécialement adaptées au maniement ou au traitement des données médicales ou de soins de santé relatives aux patients pour des données spécifiques de patients, p. ex. pour des dossiers électroniques de patients

Abrégé

This application describes, among other things, systems and methods for configuring a task-specific machine-learning model. An example method includes, based on a request to modify a machine-learning model for performing a clinical task, retrieving a corresponding node architecture defining conditional logic for performing the clinical task. The conditional logic is executed based on a first order, including an input node, an output node, and an intermediate node, of a first set of interconnected nodes including a data source node, a machine-learning model node, and a conditional logic node. A representation is generated including a first feature for configuring conditional logic of the node architecture and a second feature for configuring a parameter of a node in the first set of interconnected nodes. A selection of the first or second feature defines a second order of a second set of interconnected nodes, causing the node architecture's conditional logic to be updated.

Classes IPC  ?

• G16H 50/20 - TIC spécialement adaptées au diagnostic médical, à la simulation médicale ou à l’extraction de données médicalesTIC spécialement adaptées à la détection, au suivi ou à la modélisation d’épidémies ou de pandémies pour le diagnostic assisté par ordinateur, p. ex. basé sur des systèmes experts médicaux
• G06F 3/0482 - Interaction avec des listes d’éléments sélectionnables, p. ex. des menus
• G16H 10/60 - TIC spécialement adaptées au maniement ou au traitement des données médicales ou de soins de santé relatives aux patients pour des données spécifiques de patients, p. ex. pour des dossiers électroniques de patients

Abrégé

This application describes, among other things, machine-learning models for performing specific clinical tasks. An example method includes receiving a prompt at a first computing system in communication with a machine-learning model trained to assist in performing a clinic task that includes generating a report of a patient's medical records, guiding a patient through a care plan, creating patient care guidelines based on a patient's health profile, identifying patients requiring follow-up at a hospital, identifying changes in a standard of care for a disease setting, or evaluating unstructured data associated with a patient to identify a cohort of similar patients. Based on the prompt, a natural language response is generated that is responsive to the prompt and is based on an analysis by the machine-learning model of a repository of data that is determined to be relevant to the prompt. And the natural language response is provided to second computing system.

Classes IPC  ?

• G16H 50/20 - TIC spécialement adaptées au diagnostic médical, à la simulation médicale ou à l’extraction de données médicalesTIC spécialement adaptées à la détection, au suivi ou à la modélisation d’épidémies ou de pandémies pour le diagnostic assisté par ordinateur, p. ex. basé sur des systèmes experts médicaux
• G16H 15/00 - TIC spécialement adaptées aux rapports médicaux, p. ex. leur création ou leur transmission
• G16H 50/30 - TIC spécialement adaptées au diagnostic médical, à la simulation médicale ou à l’extraction de données médicalesTIC spécialement adaptées à la détection, au suivi ou à la modélisation d’épidémies ou de pandémies pour le calcul des indices de santéTIC spécialement adaptées au diagnostic médical, à la simulation médicale ou à l’extraction de données médicalesTIC spécialement adaptées à la détection, au suivi ou à la modélisation d’épidémies ou de pandémies pour l’évaluation des risques pour la santé d’une personne
• G16H 70/20 - TIC spécialement adaptées au maniement ou au traitement de références médicales concernant des pratiques ou des directives

Abrégé

This application describes, among other things, methods of selecting a task-specific machine-learning model for addressing a clinical task. An example method includes receiving a prompt from a user. Based on determining that the prompt requests assistance with a clinical task, a machine-learning model trained to select from among a plurality of task-specific machine-learning models each trained to assist with one of a plurality of clinical tasks selects a respective task-specific machine-learning model from among the plurality of task-specific machine-learning models based on the prompt. The prompt is provided to the selected task-specific machine-learning model. And a response received from the selected task-specific machine-learning model is provided to the user.

Classes IPC  ?

• G16H 40/20 - TIC spécialement adaptées à la gestion ou à l’administration de ressources ou d’établissements de santéTIC spécialement adaptées à la gestion ou au fonctionnement d’équipement ou de dispositifs médicaux pour la gestion ou l’administration de ressources ou d’établissements de soins de santé, p. ex. pour la gestion du personnel hospitalier ou de salles d’opération

Abrégé

This application describes, among other things, methods of generating task-specific agent modules based on user requests. An example method includes receiving a request from a user for performance of a specific task. In response to the request, an agent module to perform the specific task is generated. The generating includes selecting a set of agent building blocks from a plurality of available agent building blocks, where each agent building block in the plurality of available agent building blocks has a respective assigned function, and connecting the set of agent building blocks to form the agent module. The agent module is caused to be executed, and information from the request is provided to the agent module. Based on providing information from the request to the agent module, a response for performing the specific task is received and provided to the user.

Classes IPC  ?

• G16H 70/00 - TIC spécialement adaptées au maniement ou au traitement de références médicales

Abrégé

This application describes, amongst other things, methods and systems for building and deploying agents. An example method includes obtaining orchestration data about a set of task-specific components selected to provide a response to the prompt, where each respective task-specific components in the set of task-specific components is configured to assist with a respective clinical task of the one or more clinical tasks. The method further includes, determining an order in which each respective task-specific components of the set of task-specific components should be utilized to prepare a complete response to the prompt that address the one or more clinical tasks based on the obtained orchestration data about the set of task-specific components. The method also includes, in accordance with the determined order, providing first data related to the prompt to a first task-specific component and receiving a first response from the first task-specific component.

Classes IPC  ?

• G16H 40/20 - TIC spécialement adaptées à la gestion ou à l’administration de ressources ou d’établissements de santéTIC spécialement adaptées à la gestion ou au fonctionnement d’équipement ou de dispositifs médicaux pour la gestion ou l’administration de ressources ou d’établissements de soins de santé, p. ex. pour la gestion du personnel hospitalier ou de salles d’opération
• G06Q 10/0631 - Planification, affectation, distribution ou ordonnancement de ressources d’entreprises ou d’organisations
• G06Q 10/0633 - Analyse du flux de travail

Abrégé

This application describes, amongst other things, methods and systems for building and deploying agents. An example method includes obtaining orchestration data about a set of task-specific components selected to provide a response to the prompt, where each respective task-specific components in the set of task-specific components is configured to assist with a respective clinical task of the one or more clinical tasks. The method further includes, determining an order in which each respective task-specific components of the set of task-specific components should be utilized to prepare a complete response to the prompt that address the one or more clinical tasks based on the obtained orchestration data about the set of task-specific components. The method also includes, in accordance with the determined order, providing first data related to the prompt to a first task-specific component and receiving a first response from the first task-specific component.

Classes IPC  ?

• G16H 10/20 - TIC spécialement adaptées au maniement ou au traitement des données médicales ou de soins de santé relatives aux patients pour des essais ou des questionnaires cliniques électroniques
• G16H 50/20 - TIC spécialement adaptées au diagnostic médical, à la simulation médicale ou à l’extraction de données médicalesTIC spécialement adaptées à la détection, au suivi ou à la modélisation d’épidémies ou de pandémies pour le diagnostic assisté par ordinateur, p. ex. basé sur des systèmes experts médicaux
• G06F 8/34 - Programmation graphique ou visuelle
• G06F 16/332 - Formulation de requêtes
• G06N 3/045 - Combinaisons de réseaux

Abrégé

This application describes, amongst other things, methods and systems for building and deploying agents. An example method includes receiving a request for an agent that is configured to perform a specific task. A first agent type is identified from a set of agent types based on requirement(s) for performing the specific task, where each agent type of the set of agent types corresponds to a respective language model. A model component having the first agent type is generated. An implementation component is generated, the implementation component configured to communicatively couple the model component to a set of components based on the requirement(s) for performing the specific task, where the set of components include a set of data sources, a set of tools, and/or a set of output components. The agent, including the model component and the implementation component, is deployed to a working environment.

Classes IPC  ?

• G06N 20/00 - Apprentissage automatique
• G06N 3/10 - Interfaces, langages de programmation ou boîtes à outils de développement logiciel, p. ex. pour la simulation de réseaux neuronaux
• G06N 3/045 - Combinaisons de réseaux
• G16H 10/20 - TIC spécialement adaptées au maniement ou au traitement des données médicales ou de soins de santé relatives aux patients pour des essais ou des questionnaires cliniques électroniques
• G06F 8/34 - Programmation graphique ou visuelle
• G06F 16/332 - Formulation de requêtes
• G16H 50/20 - TIC spécialement adaptées au diagnostic médical, à la simulation médicale ou à l’extraction de données médicalesTIC spécialement adaptées à la détection, au suivi ou à la modélisation d’épidémies ou de pandémies pour le diagnostic assisté par ordinateur, p. ex. basé sur des systèmes experts médicaux
• G06F 3/0482 - Interaction avec des listes d’éléments sélectionnables, p. ex. des menus
• G06F 40/20 - Analyse du langage naturel
• G06F 40/30 - Analyse sémantique

Abrégé

This application describes, among other things, systems and methods for deploying a task-specific machine-learning model. An example method includes receiving a prompt associated with one or more commands and a plurality of tokens. A first task-specific machine-learning model is identified based on a first command of the one or more commands. Some or all of the tokens of the plurality of tokens are applied to a node associated with the first task-specific machine-learning model, and a plurality of restricted data is received. Based on evaluating the plurality of restricted data, a correlation between the first node and a second node is determined. If the correlation satisfies a threshold condition, a plurality of text data different than the prompt is generated, otherwise the some or all of the tokens are again provided to the first node. Some or all of the tokens are then traversed and applied to the second node.

Classes IPC  ?

• H04L 9/40 - Protocoles réseaux de sécurité

Abrégé

This application describes, among other things, methods of providing third-party interactions to a set of task-specific components. An example method includes receiving, at a user interface of a computing device, a user identifier and a prompt related to an identified clinical task. The method includes determining a set of task-specific components and a set of databases to which the user identifier has access. The method includes selecting, by a machine-learning model trained to select from among the set of task-specific components, a task-specific component from among the set of task-specific components. The method includes communicatively coupling the task-specific component to a database from the set of databases based on the prompt. The method includes providing the prompt to the task-specific component. The method includes receiving a response to the prompt generated by the task-specific component using information from the database and providing the response to a user.

Classes IPC  ?

• G16H 50/20 - TIC spécialement adaptées au diagnostic médical, à la simulation médicale ou à l’extraction de données médicalesTIC spécialement adaptées à la détection, au suivi ou à la modélisation d’épidémies ou de pandémies pour le diagnostic assisté par ordinateur, p. ex. basé sur des systèmes experts médicaux
• G06F 21/31 - Authentification de l’utilisateur

Abrégé

This application describes, among other things, methods of enabling data transforms across multiple task-specific orchestrations. An example method includes, based on determining that a prompt requests assistance with a clinical task, selecting, by a machine-learning model trained to select from among a plurality of task-specific components, a set of task-specific components based on the prompt. The method includes obtaining orchestration data about the set of task-specific components, where each task-specific component is configured to assist with a respective clinical task. The method includes determining, from the orchestration data, a data-compatibility criterion for clinical task data relating to the one or more clinical tasks. The method includes receiving the clinical task data. And the method includes, based on a determination that the clinical task data does not satisfy the data-compatibility criterion, providing a notification to a user indicating that the clinical task cannot be performed using the clinical task data.

Classes IPC  ?

• G16H 40/20 - TIC spécialement adaptées à la gestion ou à l’administration de ressources ou d’établissements de santéTIC spécialement adaptées à la gestion ou au fonctionnement d’équipement ou de dispositifs médicaux pour la gestion ou l’administration de ressources ou d’établissements de soins de santé, p. ex. pour la gestion du personnel hospitalier ou de salles d’opération
• G06F 16/34 - NavigationVisualisation à cet effet

Produits et services

Downloadable software and mobile applications for use in analyzing health information in support of making clinical decisions in the medical field; downloadable software and mobile applications for use as a smart digital assistant for connecting, operating, integrating, controlling, and managing databases in the field of electronic health records for analyzing health information in support of making clinical decisions in the medical field. Providing non-downloadable software for use in analyzing health information in support of making clinical decisions in the medical field; Providing non-downloadable software for use as a smart digital assistant for connecting, operating, integrating, controlling, and managing databases in the field of electronic health records for analyzing health information in support of making clinical decisions in the medical field.

Abrégé

The disclosure provides methods and systems for predicting an effect of a pharmaceutical agent in a test subject of a first species. Information about the test subject is input into a multi-task model comprising a plurality of parameters. The model applies the plurality of parameters to the information about the test subject through a plurality of instructions to generate, as output from the multi-task model, a plurality of outputs including a predicted effect of the pharmaceutical agent in the test subject and, for each respective cell type variable in a set of one or more cell type variables, a corresponding cell type classification. The information about the test subject includes a plurality of abundance values including, for each respective cellular constituent in a plurality of cellular constituents, a corresponding abundance value for the abundance of the respective cellular constituent in a biological sample of the test subject.

Classes IPC  ?

• G16H 70/40 - TIC spécialement adaptées au maniement ou au traitement de références médicales concernant des médicaments, p. ex. leurs effets secondaires ou leur usage prévu
• G16B 5/00 - TIC spécialement adaptées à la modélisation ou aux simulations dans la biologie des systèmes, p. ex. réseaux de régulation génétique, réseaux d’interaction entre protéines ou réseaux métaboliques
• G16B 40/20 - Analyse de données supervisée

Abrégé

An image viewing pipeline is provided as an independently releasable component of an image viewing application. Images are provided for viewing in the application using dedicated infrastructure for the image viewing pipeline that is separate from infrastructure used to provide other functionality of the image viewing application. The image viewing pipeline receives images from a DICOM image service and provides the images to a client application. The client application is operative to request images, process images, and cache images. The client application is further operative to satisfy individual image requests and may prefetch stacks of images using the image viewing pipeline.

Classes IPC  ?

• G16H 40/67 - TIC spécialement adaptées à la gestion ou à l’administration de ressources ou d’établissements de santéTIC spécialement adaptées à la gestion ou au fonctionnement d’équipement ou de dispositifs médicaux pour le fonctionnement d’équipement ou de dispositifs médicaux pour le fonctionnement à distance
• G06F 9/451 - Dispositions d’exécution pour interfaces utilisateur
• G06T 1/20 - Architectures de processeursConfiguration de processeurs p. ex. configuration en pipeline
• G06T 3/40 - Changement d'échelle d’images complètes ou de parties d’image, p. ex. agrandissement ou rétrécissement
• G16H 30/20 - TIC spécialement adaptées au maniement ou au traitement d’images médicales pour le maniement d’images médicales, p. ex. DICOM, HL7 ou PACS

Produits et services

Downloadable software in the nature of a mobile application using artificial intelligence (AI) for use in providing personal health care support services to patients, assisting patients with tracking health metrics and data, providing access to medical records and health information, communicating with healthcare providers, preparing for medical appointments, and providing personalized health insights to patients Medical services; providing medical information and patient health information; providing medical information to patients using artificial intelligence to provide personalized health insights for patients

Abrégé

A method for identifying actionable care events includes receiving data sources relating to a subject; storing data from them in a first database; generating a database comprising structured data fields and metadata fields from the sources; generating output data related to fields within the data or metadata fields; populating the database with the output data; generating criteria sets corresponding to respective actionable care events; evaluating the generated database using the criteria sets; identifying whether any of the criteria sets are not sufficiently satisfied by the database, wherein an underlying error or an indication of missing or incomplete information within the database with respect to a criteria set indicates a corresponding actionable care event; determining that other data sources within the collection do not sufficiently satisfy any of the identified criteria sets; and generating, based on the identifying and determining, a notification that at least one actionable care event applies.

Classes IPC  ?

• G16H 10/60 - TIC spécialement adaptées au maniement ou au traitement des données médicales ou de soins de santé relatives aux patients pour des données spécifiques de patients, p. ex. pour des dossiers électroniques de patients
• G16B 30/00 - TIC spécialement adaptées à l’analyse de séquences impliquant des nucléotides ou des aminoacides
• G16B 40/20 - Analyse de données supervisée
• G16H 15/00 - TIC spécialement adaptées aux rapports médicaux, p. ex. leur création ou leur transmission
• G16H 20/10 - TIC spécialement adaptées aux thérapies ou aux plans d’amélioration de la santé, p. ex. pour manier les prescriptions, orienter la thérapie ou surveiller l’observance par les patients concernant des médicaments ou des médications, p. ex. pour s’assurer de l’administration correcte aux patients
• G16H 20/40 - TIC spécialement adaptées aux thérapies ou aux plans d’amélioration de la santé, p. ex. pour manier les prescriptions, orienter la thérapie ou surveiller l’observance par les patients concernant des thérapies mécaniques, la radiothérapie ou des thérapies invasives, p. ex. la chirurgie, la thérapie laser, la dialyse ou l’acuponcture
• G16H 50/20 - TIC spécialement adaptées au diagnostic médical, à la simulation médicale ou à l’extraction de données médicalesTIC spécialement adaptées à la détection, au suivi ou à la modélisation d’épidémies ou de pandémies pour le diagnostic assisté par ordinateur, p. ex. basé sur des systèmes experts médicaux
• G16H 50/30 - TIC spécialement adaptées au diagnostic médical, à la simulation médicale ou à l’extraction de données médicalesTIC spécialement adaptées à la détection, au suivi ou à la modélisation d’épidémies ou de pandémies pour le calcul des indices de santéTIC spécialement adaptées au diagnostic médical, à la simulation médicale ou à l’extraction de données médicalesTIC spécialement adaptées à la détection, au suivi ou à la modélisation d’épidémies ou de pandémies pour l’évaluation des risques pour la santé d’une personne
• G16H 50/50 - TIC spécialement adaptées au diagnostic médical, à la simulation médicale ou à l’extraction de données médicalesTIC spécialement adaptées à la détection, au suivi ou à la modélisation d’épidémies ou de pandémies pour la simulation ou la modélisation des troubles médicaux
• G16H 50/70 - TIC spécialement adaptées au diagnostic médical, à la simulation médicale ou à l’extraction de données médicalesTIC spécialement adaptées à la détection, au suivi ou à la modélisation d’épidémies ou de pandémies pour extraire des données médicales, p. ex. pour analyser les cas antérieurs d’autres patients

Produits et services

(1) Downloadable and recorded computer software using artificial intelligence for machine learning to support clinical data platforms for diagnostics in the field oncology; Portable electronic voice-enabled smart digital assistant device, electronic devices containing recorded computer software for connecting, operating, integrating, controlling, and managing databases in the field of electronic health records relating to oncology. (1) Providing an on-line non downloadable system and online portal for medical access to patient health information; Platform as a service (PaaS) featuring computer software platforms for voice command and recognition software, speech to text conversion software, and voice-enabled software applications for information management in the field of electronic health records relating to oncology; Providing temporary use of online non-downloadable computer software for analyzing health information in support of making clinical decisions in the medical field; Providing an on-line non downloadable system using artificial intelligence for health information analysis in support of making clinical decisions in the medical field; Medical research; Software as a Service (SaaS) for the review and analysis of medical images in the fields of radiology and pathology that provides actionable insights to add clinical value, improve diagnostic decision making, efficiency, and productivity, in the field of oncology; medical and scientific research in the field of oncology; providing medical and scientific research information in the field of oncology; research and development services in the field of oncology; computer system design and analysis for use in oncology; software as a service (SAAS) employing artificial intelligence and machine learning-enabled computer software supporting clinical data platforms for use in the field of oncology; platform as a service (PAAS) employing artificial intelligence and machine learning-enabled computer software supporting clinical data platforms for use in the field of oncology; computer system design for use in oncology diagnostics and testing; software development for diagnostics and medical, clinical and research testing in the field of oncology; software as a service (SAAS) for clinical decision support in the field of oncology diagnostics and testing; platform as a service (PAAS) for clinical decision support in the field of oncology diagnostics and testing; software development for diagnostics and medical, clinical and research testing in the field of oncology.

Produits et services

(1) Downloadable and recorded computer software using artificial intelligence for machine learning to support clinical data platforms for diagnostics in the field oncology; Portable electronic voice-enabled smart digital assistant device, electronic devices containing recorded computer software for connecting, operating, integrating, controlling, and managing databases in the field of electronic health records relating to oncology. (1) Providing an on-line non downloadable system and online portal for medical access to patient health information; Platform as a service (PaaS) featuring computer software platforms for voice command and recognition software, speech to text conversion software, and voice-enabled software applications for information management in the field of electronic health records relating to oncology; Providing temporary use of online non-downloadable computer software for analyzing health information in support of making clinical decisions in the medical field; Providing an on-line non downloadable system using artificial intelligence for health information analysis in support of making clinical decisions in the medical field; Medical research; Software as a Service (SaaS) for the review and analysis of medical images in the fields of radiology and pathology that provides actionable insights to add clinical value, improve diagnostic decision making, efficiency, and productivity, in the field of oncology; medical and scientific research in the field of oncology; providing medical and scientific research information in the field of oncology; research and development services in the field of oncology; computer system design and analysis for use in oncology; software as a service (SAAS) employing artificial intelligence and machine learning-enabled computer software supporting clinical data platforms for use in the field of oncology; platform as a service (PAAS) employing artificial intelligence and machine learning-enabled computer software supporting clinical data platforms for use in the field of oncology; computer system design for use in oncology diagnostics and testing; software development for diagnostics and medical, clinical and research testing in the field of oncology; software as a service (SAAS) for clinical decision support in the field of oncology diagnostics and testing; platform as a service (PAAS) for clinical decision support in the field of oncology diagnostics and testing; software development for diagnostics and medical, clinical and research testing in the field of oncology. (2) Medical services; providing medical information in the field of oncology; medical services in the field of oncology diagnostics and testing.

Produits et services

Downloadable and recorded computer software using artificial intelligence for machine learning to support clinical data platforms for diagnostics in the fields of neurology and mental health. Providing an on-line non downloadable system and software for online portal for medical access to patient health information; Platform as a service (PaaS) featuring computer software platforms for voice command and recognition software, speech to text conversion software, and voice-enabled software applications for information management in the field of electronic health records relating to neurology and mental health; Providing temporary use of online non-downloadable computer software for analyzing health information in support of making clinical decisions in the medical field; Providing an on-line non downloadable system using artificial intelligence for health information analysis in support of making clinical decisions in the medical field; Medical research; Software as a Service (SaaS) for the review and analysis of medical images in the fields of radiology and pathology that provides actionable insights to add clinical value, improve diagnostic decision making, efficiency, and productivity, in the fields of neurology and mental health; medical and scientific research in the fields of neurology and mental health; research and development services in the fields of neurology and mental health; software as a service (SAAS) employing artificial intelligence and machine learning-enabled computer software supporting clinical data platforms for use in the fields of neurology and mental health; platform as a service (PAAS) employing artificial intelligence and machine learning-enabled computer software supporting clinical data platforms for use in the fields of neurology and mental health; providing a website featuring on-line non-downloadable software that enables users to analyze genome sequences for disease assessment, prediction of medical and clinical outcomes, medical and clinical research, and drug development; computer system design for use in neurology and mental health diagnostics and testing; platform as a service (PAAS) for operating clinical decision support in the fields of neurology and mental health diagnostics and testing; software as a service (SAAS) for clinical decision support in the fields of neurology and mental health diagnostics and testing; platform as a service (PAAS) for clinical decision support in the fields of neurology and mental health diagnostics and testing; software development for diagnostics and medical, clinical and research testing in the fields of neurology and mental health diagnostics and testing.

Produits et services

Downloadable and recorded computer software using artificial intelligence for machine learning to support clinical data platforms for diagnostics in the field oncology; Portable electronic voice-enabled smart digital assistant device, electronic devices containing recorded computer software for connecting, operating, integrating, controlling, and managing databases in the field of electronic health records relating to oncology. Providing an on-line non downloadable system and software for online portal for medical access to patient health information; Platform as a service (PaaS) featuring computer software platforms for voice command and recognition software, speech to text conversion software, and voice-enabled software applications for information management in the field of electronic health records relating to oncology; Providing temporary use of online non-downloadable computer software for analyzing health information in support of making clinical decisions in the medical field; Providing an on-line non downloadable system using artificial intelligence for health information analysis in support of making clinical decisions in the medical field; Medical research; Software as a Service (SaaS) for the review and analysis of medical images in the fields of radiology and pathology that provides actionable insights to add clinical value, improve diagnostic decision making, efficiency, and productivity, in the field of oncology; medical and scientific research in the field of oncology; providing medical and scientific research information in the field of oncology; research and development services in the field of oncology; computer system design and analysis for use in oncology; software as a service (SAAS) employing artificial intelligence and machine learning-enabled computer software supporting clinical data platforms for use in the field of oncology; platform as a service (PAAS) employing artificial intelligence and machine learning-enabled computer software supporting clinical data platforms for use in the field of oncology; computer system design for use in oncology diagnostics and testing; software as a service (SAAS) for clinical decision support in the field of oncology diagnostics and testing; platform as a service (PAAS) for clinical decision support in the field of oncology diagnostics and testing; software development for diagnostics and medical, clinical and research testing in the field of oncology.

Produits et services

Downloadable and recorded computer software using artificial intelligence for machine learning to support clinical data platforms for diagnostics in the field oncology; Portable electronic voice-enabled smart digital assistant device, electronic devices containing recorded computer software for connecting, operating, integrating, controlling, and managing databases in the field of electronic health records relating to oncology. Providing an on-line non downloadable system and software for online portal for medical access to patient health information; Platform as a service (PaaS) featuring computer software platforms for voice command and recognition software, speech to text conversion software, and voice-enabled software applications for information management in the field of electronic health records relating to oncology; Providing temporary use of online non-downloadable computer software for analyzing health information in support of making clinical decisions in the medical field; Providing an on-line non downloadable system using artificial intelligence for health information analysis in support of making clinical decisions in the medical field; Medical research; Software as a Service (SaaS) for the review and analysis of medical images in the fields of radiology and pathology that provides actionable insights to add clinical value, improve diagnostic decision making, efficiency, and productivity, in the field of oncology; medical and scientific research in the field of oncology; providing medical and scientific research information in the field of oncology; research and development services in the field of oncology; computer system design and analysis for use in oncology; software as a service (SAAS) employing artificial intelligence and machine learning-enabled computer software supporting clinical data platforms for use in the field of oncology; platform as a service (PAAS) employing artificial intelligence and machine learning-enabled computer software supporting clinical data platforms for use in the field of oncology; computer system design for use in oncology diagnostics and testing; software as a service (SAAS) for clinical decision support in the field of oncology diagnostics and testing; platform as a service (PAAS) for clinical decision support in the field of oncology diagnostics and testing; software development for diagnostics and medical, clinical and research testing in the field of oncology. Medical services; providing medical information in the field of oncology; medical services in the field of oncology diagnostics and testing.

Produits et services

(1) Downloadable and recorded computer software using artificial intelligence for machine learning to support clinical data platforms for diagnostics in the fields of neurology and mental health. (1) Providing an on-line non downloadable system and online portal for medical access to patient health information; Platform as a service (PaaS) featuring computer software platforms for voice command and recognition software, speech to text conversion software, and voice-enabled software applications for information management in the field of electronic health records relating to neurology and mental health; Providing temporary use of online non-downloadable computer software for analyzing health information in support of making clinical decisions in the medical field; Providing an on-line non downloadable system using artificial intelligence for health information analysis in support of making clinical decisions in the medical field; Medical research; Software as a Service (SaaS) for the review and analysis of medical images in the fields of radiology and pathology that provides actionable insights to add clinical value, improve diagnostic decision making, efficiency, and productivity, in the fields of neurology and mental health; medical and scientific research in the fields of neurology and mental health; research and development services in the fields of neurology and mental health; software as a service (SAAS) employing artificial intelligence and machine learning-enabled computer software supporting clinical data platforms for use in the fields of neurology and mental health; platform as a service (PAAS) employing artificial intelligence and machine learning-enabled computer software supporting clinical data platforms for use in the fields of neurology and mental health; providing a website featuring on-line non-downloadable software that enables users to analyze genome sequences for disease assessment, prediction of medical and clinical outcomes, medical and clinical research, and drug development; computer system design for use in neurology and mental health diagnostics and testing; software development for diagnostics and medical, clinical and research testing in the fields of neurology, and mental health diagnostics and testing; platform as a service (PAAS) for operating clinical decision support in the fields of neurology and mental health diagnostics and testing; software as a service (SAAS) for clinical decision support in the fields of neurology and mental health diagnostics and testing; platform as a service (PAAS) for clinical decision support in the fields of neurology and mental health diagnostics and testing; software development for diagnostics and medical, clinical and research testing in the fields of neurology and mental health diagnostics and testing.

Produits et services

Downloadable and recorded computer software using artificial intelligence for machine learning to support clinical data platforms for diagnostics in the fields of neurology and mental health. Medical services; providing medical information in the fields of neurology and mental health; medical services in the fields of neurology and mental health diagnostics and testing.

Produits et services

(1) Downloadable and recorded computer software using artificial intelligence for machine learning to support clinical data platforms for diagnostics in the fields of neurology and mental health. (1) Medical services; providing medical information in the fields of neurology and mental health; medical services in the fields of neurology and mental health diagnostics and testing.

Abrégé

A method of categorizing fusions is provided by the present disclosure. The method includes receiving labeled fusion data including at least one of DNA data or RNA data including at least one detected fusion associated with a specimen, providing the labeled fusion data to a classifier trained to generate a pathogenicity metric corresponding to pathogenicity of each detected fusion, receiving at least one pathogenicity metric from the classifier, and generating a report including one or more detected fusions included in the at least one detected fusion based on the pathogenicity metrics.

Classes IPC  ?

• G16H 50/30 - TIC spécialement adaptées au diagnostic médical, à la simulation médicale ou à l’extraction de données médicalesTIC spécialement adaptées à la détection, au suivi ou à la modélisation d’épidémies ou de pandémies pour le calcul des indices de santéTIC spécialement adaptées au diagnostic médical, à la simulation médicale ou à l’extraction de données médicalesTIC spécialement adaptées à la détection, au suivi ou à la modélisation d’épidémies ou de pandémies pour l’évaluation des risques pour la santé d’une personne
• G06F 18/214 - Génération de motifs d'entraînementProcédés de Bootstrapping, p. ex. ”bagging” ou ”boosting”
• G06F 18/25 - Techniques de fusion
• G06N 20/00 - Apprentissage automatique
• G16B 40/00 - TIC spécialement adaptées aux biostatistiquesTIC spécialement adaptées à l’apprentissage automatique ou à l’exploration de données liées à la bio-informatique, p. ex. extraction de connaissances ou détection de motifs
• G16H 70/60 - TIC spécialement adaptées au maniement ou au traitement de références médicales concernant des pathologies

Produits et services

Clinical research in the field of genomics; Medical research in the fields of oncology, neurology, cardiology, radiology, electrophysiology, and mental health, excluding pharmaceutical research and development; Medical and scientific research in the field of genomics; Medical and scientific research in the field of genomic sequencing and genomic analysis; Structural and functional analysis of genomes. Providing medical diagnostic testing that analyzes patient samples and creates genomic data from the analysis; Medical services in the fields of oncology, neurology, cardiology, radiology, electrophysiology, and mental health, excluding pharmaceutical research and development; Genetic and health information analysis and diagnostics for medical purposes.

Abrégé

Methods, systems, and software are provided for determining a relationship between a subject and a health entity. An electronic health record (EHR) for the subject is split into sections by detecting delineating section headers, and sections are subdivided into text spans. Text spans are filtered by language pattern recognition into a set of text spans having an expression related to the health entity. The natural language context of the expression in each text span in the set is evaluated to obtain a corresponding scoring representation. Scoring representations are inputted into a model comprising a plurality of parameters. The model outputs, for each text span in the set, at least a prediction that the text span is associated with the health entity. Models for determining relationships between subjects and health entities and methods for training models to determine relationships between subjects and health entities are also provided.

Classes IPC  ?

• G16H 10/20 - TIC spécialement adaptées au maniement ou au traitement des données médicales ou de soins de santé relatives aux patients pour des essais ou des questionnaires cliniques électroniques
• G06F 40/117 - ÉtiquetageAnnotation Désignation de blocChoix des attributs
• G06F 40/166 - Édition, p. ex. insertion ou suppression
• G06F 40/295 - Reconnaissance de noms propres
• G06F 40/30 - Analyse sémantique
• G06N 20/00 - Apprentissage automatique
• G16H 10/60 - TIC spécialement adaptées au maniement ou au traitement des données médicales ou de soins de santé relatives aux patients pour des données spécifiques de patients, p. ex. pour des dossiers électroniques de patients

Produits et services

(1) Clinical research in the field of genomics; Medical research in the fields of oncology, neurology, cardiology, radiology, electrophysiology, and mental health, excluding pharmaceutical research and development; Medical and scientific research in the field of genomics; Medical and scientific research in the field of genomic sequencing and genomic analysis; Structural and functional analysis of genomes. (2) Providing medical diagnostic testing that analyzes patient samples and creates genomic data from the analysis; Medical services in the fields of oncology, neurology, cardiology, radiology, electrophysiology, and mental health, excluding pharmaceutical research and development; Genetic and health information analysis and diagnostics for medical purposes.

Abrégé

A computing system includes a processor; an electronic network; and a memory having stored thereon computer-executable instructions that, when executed, cause the computing system to: process segmented tile images by: (i) predicting a respective biomarker classification, and (ii) predicting a respective tissue classification; determine, based on (i) and (ii), a predicted presence of biomarkers; and transmit the predicted presence. A non-transitory computer-readable medium includes computer-executable instructions that, when executed by a processor, cause a computer to: process segmented tile images by: (i) predicting a respective biomarker classification, and (ii) predicting a respective tissue classification; determine, based on (i) and (ii), a predicted presence of biomarkers; and transmit the predicted presence. A method includes processing a plurality of segmented tile images by: (i) predicting a respective biomarker classification, and (ii) predicting a respective tissue classification; determining, based on (i) and (ii), a predicted presence biomarkers; and transmitting the predicted presence.

Classes IPC  ?

• G06T 1/20 - Architectures de processeursConfiguration de processeurs p. ex. configuration en pipeline
• G06F 18/21 - Conception ou mise en place de systèmes ou de techniquesExtraction de caractéristiques dans l'espace des caractéristiquesSéparation aveugle de sources
• G06F 18/2431 - Classes multiples
• G06T 7/00 - Analyse d'image
• G06T 7/11 - Découpage basé sur les zones
• G06T 11/00 - Génération d'images bidimensionnelles [2D]
• G06V 10/44 - Extraction de caractéristiques locales par analyse des parties du motif, p. ex. par détection d’arêtes, de contours, de boucles, d’angles, de barres ou d’intersectionsAnalyse de connectivité, p. ex. de composantes connectées
• G06V 10/764 - Dispositions pour la reconnaissance ou la compréhension d’images ou de vidéos utilisant la reconnaissance de formes ou l’apprentissage automatique utilisant la classification, p. ex. des objets vidéo
• G06V 10/82 - Dispositions pour la reconnaissance ou la compréhension d’images ou de vidéos utilisant la reconnaissance de formes ou l’apprentissage automatique utilisant les réseaux neuronaux

Abrégé

A method for configuring a single architecture deep learning model includes receiving parameters; configuring segments; and processing segment, combination and/or fully-connected outputs to generate modeling output. A computing system comprising: a processor; and a memory having stored thereon computer-executable instructions that, when executed, cause the computing system to: receive parameters; configure segments; and process segment, combination and/or fully-connected outputs to generate modeling output. A non-transitory computer-readable medium having stored thereon computer-executable instructions that, when executed by one or more processors, cause a computer to: receive parameters; configure segments; and process segment, combination and/or fully-connected outputs to generate modeling output.

Classes IPC  ?

• G16H 50/20 - TIC spécialement adaptées au diagnostic médical, à la simulation médicale ou à l’extraction de données médicalesTIC spécialement adaptées à la détection, au suivi ou à la modélisation d’épidémies ou de pandémies pour le diagnostic assisté par ordinateur, p. ex. basé sur des systèmes experts médicaux
• G16H 30/40 - TIC spécialement adaptées au maniement ou au traitement d’images médicales pour le traitement d’images médicales, p. ex. l’édition

Abrégé

Systems and methods for predicting clinical trial enrollment at clinical sites are provided. For a respective site, a method identifies a corresponding set of patient records that satisfies the criteria for a clinical trial of interest. Based on the corresponding set of patient records, the method generates a corresponding longitudinal trajectory of patient eligibility for the clinical trial over a time period. Based on the corresponding longitudinal trajectory of patient eligibility for the clinical trial, the method predicts a corresponding set of expected values for enrollment in the clinical trial at the respective site.

Classes IPC  ?

• G16H 10/20 - TIC spécialement adaptées au maniement ou au traitement des données médicales ou de soins de santé relatives aux patients pour des essais ou des questionnaires cliniques électroniques
• G16H 10/60 - TIC spécialement adaptées au maniement ou au traitement des données médicales ou de soins de santé relatives aux patients pour des données spécifiques de patients, p. ex. pour des dossiers électroniques de patients

Abrégé

Systems and methods are provided for balancing a probe set for enriching a plurality of genomic loci. A nucleic acid probe set containing pools of nucleic acid probe species is obtained. Each probe species aligns to a different subsequence of a respective locus and includes proportions of a capture moiety conjugated version and a capture moiety-free version. Each probe species in a pool aligns to a portion of the genome that is at least 100 nucleotides away from any other probe species in the pool. Each pool in the probe set is separately analyzed against reference nucleic acid samples to obtain recovery rates and identify probe species that do not satisfy a minimum or a maximum recovery rate threshold. An adjusted version of a final design for the probe set is established by adjusting proportions of capture moiety conjugated and capture moiety-free versions for the identified probe species.

Classes IPC  ?

• C12Q 1/6811 - Méthodes de sélection pour la production ou l’élaboration d’oligonucléotides spécifiques cibles ou de molécules de liaison
• C12Q 1/6874 - Méthodes de séquençage faisant intervenir des réseaux d’acides nucléiques, p. ex. séquençage par hybridation [SBH]

Abrégé

A computer-implemented method, computing system and computer-readable medium include receiving training data and training a machine learning model to generate a cell expression map. A computer-implemented method, computing system and computer-readable medium includes receiving a histology image and a cell segmentation map and processing them using a trained machine learning model.

Classes IPC  ?

• G06V 20/69 - Objets microscopiques, p. ex. cellules biologiques ou pièces cellulaires
• G06T 3/40 - Changement d'échelle d’images complètes ou de parties d’image, p. ex. agrandissement ou rétrécissement
• G06T 7/10 - DécoupageDétection de bords
• G06V 10/46 - Descripteurs pour la forme, descripteurs liés au contour ou aux points, p. ex. transformation de caractéristiques visuelles invariante à l’échelle [SIFT] ou sacs de mots [BoW]Caractéristiques régionales saillantes
• G16H 30/40 - TIC spécialement adaptées au maniement ou au traitement d’images médicales pour le traitement d’images médicales, p. ex. l’édition

Abrégé

Systems and methods for phenotyping clinical data are provided. The method includes obtaining episodic records comprising unstructured clinical data from an electronic medical record (EMR) or electronic health record (EHR) for patients. The method also includes filtering the episodic records by language pattern recognition to identify episodic records that each includes an expression related to a clinical condition. The method also includes splitting each episodic record to obtain snippets comprising tokens. The method also includes predicting if an episodic record represents an instance of the clinical condition using a trained classifier. The trained classifier includes an aggregation function that aggregates the snippets to output a corresponding representation for the episodic record, and an interpretation function that interprets the corresponding representation to output a corresponding prediction for whether the episodic record represents an instance of the clinical condition.

Classes IPC  ?

• G16H 10/60 - TIC spécialement adaptées au maniement ou au traitement des données médicales ou de soins de santé relatives aux patients pour des données spécifiques de patients, p. ex. pour des dossiers électroniques de patients
• G06F 40/284 - Analyse lexicale, p. ex. segmentation en unités ou cooccurrence
• G16H 50/70 - TIC spécialement adaptées au diagnostic médical, à la simulation médicale ou à l’extraction de données médicalesTIC spécialement adaptées à la détection, au suivi ou à la modélisation d’épidémies ou de pandémies pour extraire des données médicales, p. ex. pour analyser les cas antérieurs d’autres patients

Produits et services

Downloadable software and mobile applications for use in analyzing health information in support of making clinical decisions in the medical field; downloadable software and mobile applications for use as a smart digital assistant for connecting, operating, integrating, controlling, and managing databases in the field of electronic health records for analyzing health information in support of making clinical decisions in the medical field. providing online non-downloadable software for use in analyzing health information in support of making clinical decisions in the medical field; providing online nondownloadable software for use as a smart digital assistant for connecting, operating, integrating, controlling, and managing databases in the field of electronic health records for analyzing health information in support of making clinical decisions in the medical field.

Produits et services

Downloadable and recorded computer software using artificial intelligence for machine learning to support clinical data platforms for diagnostics in the field of oncology; Portable electronic voice enabled smart digital assistant device embedded with recorded computer software for connecting, operating, integrating, controlling, and managing databases in the field of electronic health records relating to oncology Providing an on-line non downloadable software for enabling access to patient health information; Providing an Internet website portal featuring technology that allows users to access patient health information; Platform as a service (PaaS) featuring computer software platforms for information management that utilizes voice command and recognition software, speech to text conversion software, and voice-enabled software applications in the field of electronic health records relating to oncology; Providing temporary use of online non-downloadable computer software for analyzing health information in support of making clinical decisions in the medical field; Providing an on-line non downloadable software using artificial intelligence for health information analysis in support of making clinical decisions in the medical field; Medical research; Software as a Service (SaaS) services featuring software for the review and analysis of medical images in the fields of radiology and pathology that provides actionable insights to add clinical value, improve diagnostic decision making, efficiency, and productivity, in the field of oncology; medical and scientific research in the field of oncology; providing medical and scientific research information in the field of oncology; research and development services in the field of oncology; computer system design and analysis for use in oncology; software as a service (SAAS) services featuring software employing artificial intelligence for machine learning of clinical data in the field of oncology; platform as a service (PAAS) featuring computer software platforms employing artificial intelligence for machine learning of clinical data in the field of oncology; computer system design for use in oncology diagnostics and testing; software development for diagnostics and medical, clinical and research testing in the field of oncology; software as a service (SAAS) services featuring software used in making clinical decisions in the field of oncology diagnostics and testing; platform as a service (PAAS) featuring computer software platforms used in making clinical decisions in the field of oncology diagnostics and testing Medical services; providing medical information in the field of oncology; medical services in the field of oncology diagnostics and testing; Providing an Internet website portal featuring medical patient health information

Produits et services

Medical testing for diagnostic or treatment purposes

Produits et services

Downloadable and recorded computer software using artificial intelligence for machine learning to support clinical data platforms for diagnostics in the fields of neurology and mental health Medical services; providing medical information in the fields of neurology and mental health; medical services in the fields of neurology and mental health diagnostics and testing

Produits et services

Downloadable and recorded computer software using artificial intelligence for machine learning to support clinical data platforms for diagnostics in the field of oncology; Portable electronic voice enabled smart digital assistant device embedded with recorded computer software for connecting, operating, integrating, controlling, and managing databases in the field of electronic health records relating to oncology Providing an on-line non downloadable software for enabling access to patient health information; Providing an Internet website portal featuring technology that allows users to access patient health information; Platform as a service (PaaS) featuring computer software platforms for information management that utilizes voice command and recognition software, speech to text conversion software, and voice-enabled software applications in the field of electronic health records relating to oncology; Providing temporary use of online non-downloadable computer software for analyzing health information in support of making clinical decisions in the medical field; Providing an on-line non downloadable software using artificial intelligence for health information analysis in support of making clinical decisions in the medical field; Medical research; Software as a Service (SaaS) services featuring software for the review and analysis of medical images in the fields of radiology and pathology that provides actionable insights to add clinical value, improve diagnostic decision making, efficiency, and productivity, in the field of oncology; medical and scientific research in the field of oncology; providing medical and scientific research information in the field of oncology; research and development services in the field of oncology; computer system design and analysis for use in oncology; software as a service (SAAS) services featuring software employing artificial intelligence for machine learning of clinical data in the field of oncology; platform as a service (PAAS) featuring computer software platforms employing artificial intelligence for machine learning of clinical data in the field of oncology; computer system design for use in oncology diagnostics and testing; software development for diagnostics and medical, clinical and research testing in the field of oncology; software as a service (SAAS) services featuring software used in making clinical decisions in the field of oncology diagnostics and testing; platform as a service (PAAS) featuring computer software platforms used in making clinical decisions in the field of oncology diagnostics and testing Providing an Internet website portal featuring medical patient health information

Produits et services

Downloadable and recorded computer software using artificial intelligence for machine learning to support clinical data platforms for diagnostics in the fields of neurology and mental health Providing an on-line non downloadable software for enabling access to patient health information; Providing an Internet website portal featuring technology that allows users to access patient health information; Platform as a service (PaaS) featuring computer software platforms for information management that utilizes voice command and recognition software, speech to text conversion software, and voice-enabled software applications in the field of electronic health records relating to neurology and mental health; Providing temporary use of online non-downloadable computer software for analyzing health information in support of making clinical decisions in the medical field; Providing an on-line non downloadable software using artificial intelligence for health information analysis in support of making clinical decisions in the medical field; Medical research; Software as a Service (SaaS) services featuring software for the review and analysis of medical images in the fields of radiology and pathology that provides actionable insights to add clinical value, improve diagnostic decision making, efficiency, and productivity, in the fields of neurology and mental health; medical and scientific research in the fields of neurology and mental health; research and development services in the fields of neurology and mental health; software as a service (SAAS) services featuring software employing artificial intelligence for machine learning of clinical data in the fields of neurology and mental health; platform as a service (PAAS) featuring computer software platforms employing artificial intelligence for machine learning of clinical data in the fields of neurology and mental health; providing a website featuring on-line non-downloadable software that enables users to analyze genome sequences for disease assessment, prediction of medical and clinical outcomes, medical and clinical research, and drug development; computer system design for use in neurology and mental health diagnostics and testing; software development for diagnostics and medical, clinical and research testing in the fields of neurology, and mental health diagnostics and testing; platform as a service (PAAS) featuring computer software platforms used in making clinical decisions in the fields of neurology and mental health diagnostics and testing; software as a service (SAAS) services featuring software used in making clinical decisions in the fields of neurology and mental health diagnostics and testing; software development for diagnostics and medical, clinical and research testing in the fields of neurology and mental health diagnostics and testing

Abrégé

The following relates generally to determining genomic biomarkers from biological data (e.g., a read of a nucleic acid, or an image, such as an image of a slide). In some embodiments, a transform orchestrator: (i) receives an order to transform biological data to one or more genomic biomarkers; (ii) selects a transform for deriving each of the received one or more genomic biomarkers; (iii) associates each selected transform with a cloud computing platform; (iv) executes instructions for each selected transform; (v) communicates an operational status of each selected transform; (vi) stores the genomic biomarker output from each selected transform; and (vii) provides a notification of a final operational status of each selected transform.

Classes IPC  ?

• G16B 20/00 - TIC spécialement adaptées à la génomique ou protéomique fonctionnelle, p. ex. corrélations génotype-phénotype
• G16B 40/20 - Analyse de données supervisée

Abrégé

Methods, systems, and software are provided for detecting gene fusions in a subject with a cancer condition through mRNA boundary analysis of next generation sequencing of a transcriptome or relevant part thereof. Methods, systems, and software are provided for detecting splice variants in a subject with a cancer condition through mRNA boundary analysis of next generation sequencing of a transcriptome or relevant part thereof. Methods, systems, and software are provided for evaluating the complexity of an RNA-seq sequencing reaction through mRNA boundary analysis. Generally, the methods described herein include obtaining sequences of mRNA molecules for a plurality of genes in a sample of a subject. For each gene, an RNA boundary distribution including relative abundance value for each respective RNA boundary sub-sequence of the gene is determined from the plurality of sequences. These abundance values are evaluated using one or more models to provide the analyses described herein.

Classes IPC  ?

• C12Q 1/6886 - Produits d’acides nucléiques utilisés dans l’analyse d’acides nucléiques, p. ex. amorces ou sondes pour les maladies provoquées par des altérations du matériel génétique pour le cancer
• G06N 20/10 - Apprentissage automatique utilisant des méthodes à noyaux, p. ex. séparateurs à vaste marge [SVM]
• G16B 20/20 - Détection d’allèles ou de variantes, p. ex. détection de polymorphisme d’un seul nucléotide
• G16B 25/10 - Profilage de l’expression de gènes ou de protéinesEstimation ou normalisation de ratio d’expression
• G16B 40/20 - Analyse de données supervisée
• G16H 20/00 - TIC spécialement adaptées aux thérapies ou aux plans d’amélioration de la santé, p. ex. pour manier les prescriptions, orienter la thérapie ou surveiller l’observance par les patients
• G16H 50/20 - TIC spécialement adaptées au diagnostic médical, à la simulation médicale ou à l’extraction de données médicalesTIC spécialement adaptées à la détection, au suivi ou à la modélisation d’épidémies ou de pandémies pour le diagnostic assisté par ordinateur, p. ex. basé sur des systèmes experts médicaux

Produits et services

Downloadable and recorded computer software and mobile applications for use in providing recommendations for patient treatment and care in the fields of cardiology, oncology, and neurology; downloadable and recorded computer software and mobile applications featuring artificial intelligence for machine learning and predictive analytics to support clinical data platforms for patient diagnosis and treatment in the fields of cardiology, oncology, and neurology. Software as a service (SAAS) services featuring online non-downloadable software for use in providing recommendations for patient treatment and care in the fields of oncology and neurology; software as a service (SAAS) services featuring software using artificial intelligence for machine learning and predictive analytics to support clinical data platforms for patient diagnosis and treatment in the fields oncology and neurology. Medical services; providing medical information in the fields of oncology and neurology.

Produits et services

Software as a service (SAAS) featuring online non-downloadable software for use in providing recommendations for patient treatment and care in the field of cardiology; software as a service (SAAS) featuring artificial intelligence for machine learning and predictive analytics to support clinical data platforms for patient diagnosis and treatment in the field of cardiology. Medical services; providing medical information in the field of cardiology.

Produits et services

(1) Downloadable and recorded computer software and mobile applications for use in providing recommendations for patient treatment and care in the fields of cardiology, oncology, and neurology; downloadable and recorded computer software and mobile applications featuring artificial intelligence for machine learning and predictive analytics to support clinical data platforms for patient diagnosis and treatment in the fields of cardiology, oncology, and neurology. (1) Software as a service (SAAS) featuring online non-downloadable software for use in providing recommendations for patient treatment and care in the fields of oncology and neurology; software as a service (SAAS) featuring artificial intelligence for machine learning and predictive analytics to support clinical data platforms for patient diagnosis and treatment in the fields oncology and neurology. (2) Medical services; providing medical information in the fields of oncology and neurology.

Produits et services

(1) Software as a service (SAAS) featuring online non-downloadable software for use in providing recommendations for patient treatment and care in the field of cardiology; software as a service (SAAS) featuring artificial intelligence for machine learning and predictive analytics to support clinical data platforms for patient diagnosis and treatment in the field of cardiology. (2) Medical services; providing medical information in the field of cardiology.

Produits et services

Downloadable and recorded computer software using artificial intelligence for machine learning to support clinical data platforms for cardiac diagnostics and electrophysiology.

Produits et services

(1) Downloadable and recorded computer software using artificial intelligence for machine learning to support clinical data platforms for cardiac diagnostics and electrophysiology.