Abrégé

A model-assisted system for determining a patient event date may include a processor. The processor may be programmed to access a database storing a medical record associated with a patient, the medical record comprising unstructured data; analyze the unstructured data to identify a plurality of snippets of information in the medical record associated with a patient event; determine a date associated with each of the plurality of snippets; identify a plurality of query periods associated with the patient event; and generate, for each of the query periods, a probability of whether the patient event occurred during the query period based on the plurality of snippets and the associated dates.

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

Described herein are techniques of using machine learning to automatically extract clinical variable values for subjects from clinical record data. The techniques designate certain clinical variables as hybrid variables that can be assigned values by machine learning model prediction. The techniques process, using a machine learning model trained to predict a value of a hybrid variable, clinical record data associated with a subject to obtain a predicted hybrid variable value and an associated confidence score. The techniques set the value of the hybrid variable for the subject to the predicted hybrid variable value when the model prediction is of sufficiently high confidence.

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 16/35 - Groupement; Classement
• G06N 20/00 - Apprentissage automatique

Produits et services

Software as a service (SAAS) services featuring software for visualization tools and dashboards for efficiently analyzing real-world data (RWD), generating clinical and commercial hypotheses, and facilitating team-based collaborative analysis in the field of oncology and healthcare

Abrégé

Described herein are methods and systems for receiving electronic messages comprising laboratory results and associating the laboratory results with appropriate laboratory result identifiers to generate mapped laboratory results. The mapped laboratory results may be used to inform clinical patient treatment and/or clinical trial design or performance.

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
• G16H 50/20 - TIC spécialement adaptées au diagnostic médical, à la simulation médicale ou à l’extraction de données médicales; TIC 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 information and advancing understanding of innovation and research in the field of oncology and improving the quality of cancer care

Abrégé

Systems and methods may manage one or more clinical trials. In one implementation, a system for creating a query for a trial includes at least one processor programmed to: cause a computing device associated with a sponsor of the trial to display a graphical user interface comprising a plurality of patient identifiers associated with the trial; receive, from the computing device, a selection of one of the plurality of patient identifiers; receive, from the computing device, a query comprising an inquiry relating to the selected patient identifier; and transmit the query to a practice site associated with the selected patient identifier.

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 3/0482 - Interaction avec des listes d’éléments sélectionnables, p.ex. des menus
• G06F 16/2455 - Exécution des 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

Abrégé

A system for standardizing medical testing data may include a processor. The processor may be programmed to access a first medical testing record including a first data element represented in a first data format; access a second medical testing record including a second data element represented in a second data format, the second data format being different from the first data format; determine that the first data element and the second data element are associated with a common value classifier; and store the first data element and the second data element in a database in association with the common value classifier.

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
• G16B 45/00 - TIC spécialement adaptées à la visualisation de données liées à la bio-informatique, p. ex. affichage de cartes ou de réseaux
• 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 15/00 - TIC spécialement adaptées aux rapports médicaux, p.ex. leur création ou leur transmission
• G16H 50/20 - TIC spécialement adaptées au diagnostic médical, à la simulation médicale ou à l’extraction de données médicales; TIC 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

Association services, namely, promoting the interests of members of a consortium in the field of pharmaceuticals in connection with the benefits of real world evidence and real world data in the regulatory process; Association services, namely, promoting the interests of pharmaceutical researchers

Produits et services

Collection and compilation of information into computer databases in the fields of oncology, medicine, life science and healthcare; business data analysis services in the fields of oncology, medicine, life science, and healthcare, namely preparing datasets and compilations of healthcare data for business purposes; database management; electronic data collection and data submission services for business purposes in the fields of oncology, medicine, life science and healthcare; business consulting and management in the field of oncology patient data for business purposes

Abrégé

Described herein is a graphical intervention test development system. The graphical intervention test development system provides a graphical intervention test development environment that facilitates computer-based design of an intervention test. The graphical intervention test development environment provides a graphical user interface (GUI) and visualizations of various aspects of an intervention test therein. The graphical intervention test development environment further provides a control interface through which a user can manipulate control parameters that affect outcomes of the intervention test.

Classes IPC  ?

• G06F 9/44 - Dispositions pour exécuter des programmes spécifiques
• G06F 11/36 - Prévention d'erreurs en effectuant des tests ou par débogage de logiciel

Abrégé

A model-assisted system for predicting survivability of a patient may include at least one processor. The processor may be programmed to access a database storing a medical record for the patient. The medical record may include at least one of structured and unstructured information relative to the patient and may lack a structured patient ECOG score. The processor may be further programmed to analyze at least one of the structured and unstructured information relative to the patient; based on the analysis, and in the absence of a structured ECOG score, generate a performance status prediction for the patient; and provide an output indicative of the predicted performance status. The analysis of at least one of the structured and unstructured information and the generation of the predicted performance status may be performed by at least one of a trained machine learning model or a natural language processing algorithm.

Classes IPC  ?

• G06N 5/04 - Modèles d’inférence ou de raisonnement
• G06N 5/02 - Représentation de la connaissance; Représentation symbolique
• G06N 20/00 - Apprentissage automatique
• 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 50/50 - TIC spécialement adaptées au diagnostic médical, à la simulation médicale ou à l’extraction de données médicales; TIC 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 médicales; TIC 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

A system for tracking adverse events may include at least one processing device programmed to receive a request from a user to record an adverse event experienced by a patient; receive a search term input by the user; identify, in an adverse event database and based on the search term, at least one database record for an adverse event, wherein the at least one database record includes an adverse event type and at least one characteristic; receive, via an input field, a rating of the at least one characteristic for the patient; generate an adverse event record based on the adverse event type and the rating; and store the adverse event record in an adverse event log.

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 16/2457 - Traitement des requêtes avec adaptation aux besoins de l’utilisateur
• G06F 16/248 - Présentation des résultats de requêtes
• G16H 15/00 - TIC spécialement adaptées aux rapports médicaux, p.ex. leur création ou leur transmission
• 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
• H04L 9/32 - Dispositions pour les communications secrètes ou protégées; Protocoles réseaux de sécurité comprenant des moyens pour vérifier l'identité ou l'autorisation d'un utilisateur du système

Abrégé

Described herein are techniques of using machine learning to automatically extract clinical variable values for subjects from clinical record data. The techniques designate certain clinical variables as hybrid variables that can be assigned values by machine learning model prediction. The techniques process, using a machine learning model trained to predict a value of a hybrid variable, clinical record data associated with a subject to obtain a predicted hybrid variable value and an associated confidence score. The techniques set the value of the hybrid variable for the subject to the predicted hybrid variable value when the model prediction is of sufficiently high confidence.

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

Described herein are techniques of using machine learning to automatically extract clinical variable values for subjects from clinical record data. The techniques designate certain clinical variables as hybrid variables that can be assigned values by machine learning model prediction. The techniques process, using a machine learning model trained to predict a value of a hybrid variable, clinical record data associated with a subject to obtain a predicted hybrid variable value and an associated confidence score. The techniques set the value of the hybrid variable for the subject to the predicted hybrid variable value when the model prediction is of sufficiently high confidence.

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 16/35 - Groupement; Classement
• G06N 20/00 - Apprentissage automatique

Abrégé

Methods and systems for interacting with and/or managing a clinical trial protocol via an electronic clinical trial protocol management system. The electronic clinical trial protocol management system capable of handling one or more treatment cycles within the clinical trial protocol, and having one or more occurrence programmatic elements and one or more treatment cycle programmatic elements. Each occurrence programmatic element includes a formulaic representation of scheduling of a clinical procedure within one or more treatment cycles. Procedure programmatic elements are related to one or more iterations of a treatment cycle programmatic element. Via such a relationship it is possible to determine a proper clinical procedure applicable to a subject at a position in a clinical trial using an occurrence programmatic element.

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 70/20 - TIC spécialement adaptées au maniement ou au traitement de références médicales concernant des pratiques ou des directives

Abrégé

A model-assisted system for determining probabilities associated with a patient attribute. The processor may be programmed to access a database storing an unstructured medical record associated with a patient and analyze the medical record to identify snippets of information associated with the patient attribute. The processor may generate, based on each snippet, a snippet vector comprising a plurality of snippet vector elements comprising weight values associated with at least one word included in the snippet. The processor may analyze the snippet vectors to generate a summary vector comprising a plurality of summary vector elements, wherein each of the plurality of summary vector elements is associated with a corresponding snippet vector element and is determined based on an analysis of the corresponding snippet vector element. The processor may further generate, based on the summary vector, at least one output indicative of a probability associated with the patient attribute.

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 50/30 - TIC spécialement adaptées au diagnostic médical, à la simulation médicale ou à l’extraction de données médicales; TIC 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
• G06N 3/044 - Réseaux récurrents, p.ex. réseaux de Hopfield
• G06N 7/01 - Modèles graphiques probabilistes, p.ex. réseaux probabilistes

Abrégé

A model-assisted system for processing data to extract a patient event date may include a processor. The processor may be programmed to access a database storing a medical record associated with a patient, the medical record comprising unstructured data; analyze the unstructured data to identify a plurality of dates represented in at least one document included in the medical record; identify a plurality of snippets of information included in the at least one document, each snippet of the plurality of snippets being associated with a date of the plurality of dates; inputting the plurality of snippets into a machine learning model, the machine learning model having been trained to determine associations between dates and patient events based on a training set of snippet data; and determine whether each date of the plurality of dates is associated with a patient event based on an output of the machine learning model.

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
• G06N 3/08 - Méthodes d'apprentissage

Abrégé

A model-assisted system for processing data to extract a patient event date may include a processor. The processor may be programmed to access a database storing a medical record associated with a patient, the medical record comprising unstructured data; analyze the unstructured data to identify a plurality of dates represented in at least one document included in the medical record; identify a plurality of snippets of information included in the at least one document, each snippet of the plurality of snippets being associated with a date of the plurality of dates; inputting the plurality of snippets into a machine learning model, the machine learning model having been trained to determine associations between dates and patient events based on a training set of snippet data; and determine whether each date of the plurality of dates is associated with a patient event based on an output of the machine learning model.

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 50/00 - TIC spécialement adaptées au diagnostic médical, à la simulation médicale ou à l’extraction de données médicales; TIC spécialement adaptées à la détection, au suivi ou à la modélisation d’épidémies ou de pandémies

Abrégé

A model-assisted system and method for predicting health care services. In one implementation, a model-assisted system may comprise a least one processor programmed to access a database storing a medical record associated with a patient and analyze the medical record to identify a characteristic of the patient. The processor may determine a patient risk level indicating a likelihood that the patient will require a health care service within a predetermined time period; compare the patient risk level to a predetermined risk threshold; and generate a report indicating a recommended intervention for the patient. The processor may further determine a calibration factor indicating a difference between an average patient risk level and an average actual healthcare service usage for a first group of patients; and determine, based on the calibration factor, a bias relative to a second group of patients.

Classes IPC  ?

• G16H 50/30 - TIC spécialement adaptées au diagnostic médical, à la simulation médicale ou à l’extraction de données médicales; TIC 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 70/00 - TIC spécialement adaptées au maniement ou au traitement de références médicales
• G06N 20/00 - Apprentissage automatique
• G16H 50/20 - TIC spécialement adaptées au diagnostic médical, à la simulation médicale ou à l’extraction de données médicales; TIC 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 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 50/70 - TIC spécialement adaptées au diagnostic médical, à la simulation médicale ou à l’extraction de données médicales; TIC 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 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

Platform as a service (PAAS) featuring computer software platforms for design, planning, and implementation of clinical trials, setting and submitting protocols, recruiting participants; platform as a service (PAAS) featuring computer software platforms for use in analyzing real world clinical data for use in research in the fields of oncology, medicine, and healthcare; providing temporary use of online non-downloadable software for simulating outcomes based on real world clinical data, ensuring data quality, modeling outcomes, and visualizing data; consulting services for others in the field of design, planning, and implementation of clinical trials

Produits et services

Downloadable cloud-computing software for transferring patient data from a source system to a target system in furtherance of clinical trial studies and research Data processing services in the field of clinical trial studies and research Providing online non-downloadable computer software platforms for transferring patient data from a source system to a target system in furtherance of clinical trial studies and research

Produits et services

Downloadable cloud-based software for data management; Downloadable cloud-based software to collect, compile, capture, record, store, organize, process, manage, share, and report data, research, clinical research, clinical data, and research and data from clinical trials Electronic storage of documents and data; software as a service (SAAS) and platform as a service (PAAS) services featuring software for data management; software as a service (SAAS) and platform as a service (PAAS) services featuring software to collect, compile, capture, record, store, organize, process, manage, share, and report data, research, clinical research, clinical data, and research and data from clinical trials; providing temporary use of online non-downloadable software for data management; providing temporary use of online non-downloadable software to collect, compile, capture, record, store, organize, process, manage, share, and report data, research, clinical research, clinical data, and research and data from clinical trials; Providing temporary use of nondownloadable cloud- based software for data management; Providing temporary use of non-downloadable cloud-based cloud computing featuring software to collect, compile, capture, record, store, organize, process, manage, share, and report data, research, clinical research, clinical data, and research and data from clinical trials; design and development of software, software as a service (SAAS) services, and platform as a service (PAAS) services

Abrégé

A system for classifying patient parameter values may include at least one processor programmed to access first information associated with a plurality of patients, the first information including a plurality of patient parameters associated with the plurality of patients, the first information being accessed electronically via a database; determine a first value associated with a patient parameter of at least one of the plurality of patients; analyze second information associated with at least one patient to determine a second value of the patient parameter; detect, based on analysis of at least the first value and the second value, a potential anomaly in the second value; and cause a graphical user interface of a computing device to display at least one graphical element indicating the potential anomaly.

Classes IPC  ?

• G16H 50/70 - TIC spécialement adaptées au diagnostic médical, à la simulation médicale ou à l’extraction de données médicales; TIC 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
• G06N 3/08 - Méthodes d'apprentissage
• G06F 16/23 - Mise à jour
• 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

A model-assisted system for determining a patient event date may include a processor. The processor may be programmed to access a database storing a medical record associated with a patient, the medical record comprising unstructured data; analyze the unstructured data to identify a plurality of snippets of information in the medical record associated with a patient event; determine a date associated with each of the plurality of snippets; identify a plurality of query periods associated with the patient event; and generate, for each of the query periods, a probability of whether the patient event occurred during the query period based on the plurality of snippets and the associated dates.

Classes IPC  ?

• G06F 16/33 - 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
• G06N 20/00 - Apprentissage automatique

Abrégé

A model-assisted system for determining a patient event date may include a processor. The processor may be programmed to access a database storing a medical record associated with a patient, the medical record comprising unstructured data; analyze the unstructured data to identify a plurality of snippets of information in the medical record associated with a patient event; determine a date associated with each of the plurality of snippets, identify a plurality of query periods associated with the patient event; and generate, for each of the query periods, a probability of whether the patient event occurred during the query period based on the plurality of snippets and the associated dates.

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

Systems and methods may manage one or more clinical trials. In one implementation, a system for creating a query for a trial includes at least one processor programmed to: cause a computing device associated with a sponsor of the trial to display a graphical user interface comprising a plurality of patient identifiers associated with the trial; receive, from the computing device, a selection of one of the plurality of patient identifiers; receive, from the computing device, a query comprising an inquiry relating to the selected patient identifier; and transmit the query to a practice site associated with the selected patient identifier.

Classes IPC  ?

• G06F 16/14 - Systèmes de fichiers; Serveurs de fichiers - Détails de la recherche de fichiers basée sur les métadonnées des fichiers
• G06Q 10/10 - Bureautique; Gestion du temps
• 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 16/2458 - Types spéciaux de requêtes, p.ex. requêtes statistiques, requêtes floues ou requêtes distribuées
• 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 may manage one or more clinical trials. In one implementation, a system for creating a query for a trial includes at least one processor programmed to: cause a computing device associated with a sponsor of the trial to display a graphical user interface comprising a plurality of patient identifiers associated with the trial; receive, from the computing device, a selection of one of the plurality of patient identifiers; receive, from the computing device, a query comprising an inquiry relating to the selected patient identifier; and transmit the query to a practice site associated with the selected patient identifier.

Classes IPC  ?

• G06F 3/048 - Techniques d’interaction fondées sur les interfaces utilisateur graphiques [GUI]
• 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
• G06F 16/2455 - Exécution des requêtes
• G06F 3/0482 - Interaction avec des listes d’éléments sélectionnables, p.ex. des menus

Abrégé

A model-assisted system for identifying a group of patients for a cohort using a generalized biomarker model may include a processor programmed to provide, to a generalized biomarker model, a first biomarker associated with a cohort, the generalized biomarker model being trained based on one or more second biomarkers; receive, from the generalized biomarker model, an output indicating a plurality of individuals with associated likelihoods of at least one of: having an attribute associated with the third biomarker or having been tested for the attribute associated with the first biomarker; determine a likelihood threshold based on a predetermined cohort size associated with the first biomarker and identify, based on the output, a group of the plurality of individuals for inclusion in a cohort, each individual in the group of the plurality of individuals being associated with a likelihood received from the generalized biomarker model that satisfies the likelihood threshold.

Classes IPC  ?

• G16H 50/20 - TIC spécialement adaptées au diagnostic médical, à la simulation médicale ou à l’extraction de données médicales; TIC 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 system for generating dynamic multi-layered graphical user interfaces may include at least one processing device configured to retrieve treatment regimen data for a patient from at least one networked database, retrieve patient attribute data for the patient, generate a graphical user interface reflecting a medication identified in the treatment regimen data and one or more associated patient attributes, compare the extracted patient attributes to one or more thresholds in a rule set, the rule set correlating a dose of the medication to one or more expected values of the extracted patient attributes, evaluating at least one condition of at least rule in the rule set, generate, based on the evaluation an interactive icon for display in the graphical user interface, and generate an overlay window displayed adjacent the interactive icon, the overlay window providing details associated with the interactive icon.

Classes IPC  ?

• 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 comport
• 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 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 3/0482 - Interaction avec des listes d’éléments sélectionnables, p.ex. des menus

Abrégé

A model-assisted system for extracting patient information. A processor may be programmed to access a database storing one or more medical records associated with a patient and determine, using a first machine learning model and based on unstructured information included in the one or more medical records, whether the patient is associated with a condition. The processor may further be programmed to identify a date associated with the patient and determine, using a second machine learning model and based on the unstructured information, whether the patient is associated with the condition relative to the date. The processor may generate an output indicating whether the patient is associated with the condition and whether the patient is associated with the condition relative to the date.

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 15/00 - TIC spécialement adaptées aux rapports médicaux, p.ex. leur création ou leur transmission
• G16H 50/20 - TIC spécialement adaptées au diagnostic médical, à la simulation médicale ou à l’extraction de données médicales; TIC 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 model-assisted system for extracting patient information. A processor may be programmed to access a database storing one or more medical records associated with a patient and determine, using a first machine learning model and based on unstructured information included in the one or more medical records, whether the patient is associated with a condition. The processor may further be programmed to identify a date associated with the patient and determine, using a second machine learning model and based on the unstructured information, whether the patient is associated with the condition relative to the date. The processor may generate an output indicating whether the patient is associated with the condition and whether the patient is associated with the condition relative to the date.

Classes IPC  ?

• G16H 50/70 - TIC spécialement adaptées au diagnostic médical, à la simulation médicale ou à l’extraction de données médicales; TIC 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 40/20 - TIC spécialement adaptées à la gestion ou à l’administration de ressources ou d’établissements de santé; TIC 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 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 50/20 - TIC spécialement adaptées au diagnostic médical, à la simulation médicale ou à l’extraction de données médicales; TIC 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 16/33 - Requêtes
• G06N 20/00 - Apprentissage automatique

Produits et services

providing temporary use of online non-downloadable middleware that converts and transfers protected information to viewable research data; providing temporary use of cloud-based middleware that converts and transfers protected information to viewable research data; design and development of software, middleware, software as a service (SAAS) services, and platform as a service (PAAS) services

Produits et services

Electronic storage of documents and data; software as a service (SAAS) and platform as a service (PAAS) services featuring software for data management; software as a service (SAAS) and platform as a service (PAAS) services featuring software to collect, compile, capture, record, store, organize, process, manage, share, and report data, research, clinical research, clinical data, and research and data from clinical trials; providing temporary use of online non-downloadable software for data management; providing temporary use of online non-downloadable software to collect, compile, capture, capture, store, organize, process, manage, share, and report data, research, clinical research, clinical data, and research and data from clinical trials; providing temporary use of online non-downloadable middleware for providing an interface between electronic health records or electronic medical records and electronic data capture systems; providing temporary use of cloud-based middleware for providing an interface between electronic health records or electronic medical records and electronic data capture systems; providing temporary use of online non-downloadable middleware for providing an interface between electronic health records or electronic medical records and electronic data capture systems to convert and transfer protected health information to viewable research data; providing temporary use of cloud-based middleware for providing an interface between electronic health records or electronic medical records and electronic data capture systems to convert and transfer protected health information to viewable research data; providing temporary use of online non-downloadable middleware that converts and transfers protected information to viewable research data; providing temporary use of cloud-based middleware that converts and transfers protected information to viewable research data; Provide temporary use of online non-downloadable software that transfers electronic health records, electronic medical records, and medical data to cloud-based servers; provide temporary use of online non-downloadable software to review, monitor, audit, query, and organize electronic health records and electronic medical records; provide temporary use of online non-downloadable software for creating and downloading reports and metrics reports based on data captured from electronic health records and electronic medical records; provide temporary use of online non-downloadable software that tags and tracks metadata; software as a service (SAAS) and platform as a service (PAAS) services featuring software that transfers electronic health records, electronic medical records, and medical data to cloud-based servers; software as a service (SAAS) and platform as a service (PAAS) services featuring software to review, monitor, audit, query, and organize electronic health records and electronic medical records; software as a service (SAAS) and platform as a service (PAAS) services featuring software for creating and downloading reports and metrics reports based on data captured from electronic health records and electronic medical records; software as a service (SAAS) and platform as a service (PAAS) services featuring software that tags and tracks metadata; Providing temporary use of non-downloadable cloud- based software to review, monitor, audit, query, and organize electronic health records and electronic medical records; Providing temporary use of non-downloadable cloud-based software for creating and downloading reports and metrics reports based on data captured from electronic health records and electronic medical records; Providing temporary use of non-downloadable cloud-based software that tags and tracks metadata; Software as a service (SAAS) services featuring software for managing and compiling data for use in clinical trial research; providing temporary use of online non-downloadable software for the management, storage, exchange, and review of secured documents, secured data, and clinical research regulatory compliance documentation; software as a service (SAAS) and platform as a service (PAAS) services featuring software for the management, storage, exchange, and review of secured documents, secured data, and regulatory compliance documentation; design and development of software, middleware, and software as a service (SAAS) services, and platform as a service (PAAS) services

Produits et services

Providing temporary use of online non-downloadable middleware for providing an interface between electronic health records or electronic medical records and electronic data capture systems; providing temporary use of cloud-based middleware for providing an interface between electronic health records or electronic medical records and electronic data capture systems; providing temporary use of online non-downloadable middleware for providing an interface between electronic health records or electronic medical records and electronic data capture systems to convert and transfer protected health information to viewable research data; providing temporary use of cloud-based middleware for providing an interface between electronic health records or electronic medical records and electronic data capture systems to convert and transfer protected health information to viewable research data; providing temporary use of online non-downloadable middleware that converts and transfers protected information to viewable research data; providing temporary use of cloud-based middleware that converts and transfers protected information to viewable research data; design and development of software, middleware, software as a service (SAAS) services, and platform as a service (PAAS) services

Abrégé

A model-assisted system and method for predicting health care services. In one implementation, a model-assisted system may comprise a least one processor programmed to access a database storing a medical record associated with a patient and analyze the medical record to identify a characteristic of the patient. The processor may determine a patient risk level indicating a likelihood that the patient will require a health care service within a predetermined time period; compare the patient risk level to a predetermined risk threshold; and generate a report indicating a recommended intervention for the patient. The processor may further determine a calibration factor indicating a difference between an average patient risk level and an average actual healthcare service usage for a first group of patients; and determine, based on the calibration factor, a bias relative to a second group of patients.

Classes IPC  ?

• G16H 50/30 - TIC spécialement adaptées au diagnostic médical, à la simulation médicale ou à l’extraction de données médicales; TIC 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
• G16H 50/70 - TIC spécialement adaptées au diagnostic médical, à la simulation médicale ou à l’extraction de données médicales; TIC 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
• G06N 20/00 - Apprentissage automatique
• G16H 70/00 - TIC spécialement adaptées au maniement ou au traitement de références médicales
• 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 50/20 - TIC spécialement adaptées au diagnostic médical, à la simulation médicale ou à l’extraction de données médicales; TIC 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 model-assisted system and method for predicting health care services. In one implementation, a model-assisted system may comprise a least one processor programmed to access a database storing a medical record associated with a patient and analyze the medical record to identify a characteristic of the patient. The processor may determine a patient risk level indicating a likelihood that the patient will require a health care service within a predetermined time period; compare the patient risk level to a predetermined risk threshold; and generate a report indicating a recommended intervention for the patient. The processor may further determine a calibration factor indicating a difference between an average patient risk level and an average actual healthcare service usage for a first group of patients; and determine, based on the calibration factor, a bias relative to a second group of patients.

Classes IPC  ?

• G16H 40/20 - TIC spécialement adaptées à la gestion ou à l’administration de ressources ou d’établissements de santé; TIC 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 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
• G06N 20/00 - Apprentissage automatique
• G06Q 40/08 - Assurance
• 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 15/00 - TIC spécialement adaptées aux rapports médicaux, p.ex. leur création ou leur transmission
• G16H 50/20 - TIC spécialement adaptées au diagnostic médical, à la simulation médicale ou à l’extraction de données médicales; TIC 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 médicales; TIC 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 50/70 - TIC spécialement adaptées au diagnostic médical, à la simulation médicale ou à l’extraction de données médicales; TIC 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 20/00 - 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

Abrégé

A model-assisted system for identifying candidates for a cohort based on a biomarker may include at least one processor. The processor may be programmed to access a database from which information associated with a population of individuals can be derived; provide, to a generalized biomarker model, a first biomarker associated with a cohort, the generalized biomarker model being trained based on one or more second biomarkers using the information, wherein the first biomarker is different from the one or more second biomarkers; receive, from the generalized biomarker model, a first output indicating a first group of the population of individuals exceeding a first likelihood threshold of having been tested for the first biomarker; and determine, based on the first output, whether an individual from among the first group of the population of individuals is a candidate for the cohort.

Classes IPC  ?

• G16H 50/70 - TIC spécialement adaptées au diagnostic médical, à la simulation médicale ou à l’extraction de données médicales; TIC 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 16/35 - Groupement; Classement
• 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
• G16H 50/50 - TIC spécialement adaptées au diagnostic médical, à la simulation médicale ou à l’extraction de données médicales; TIC 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

Abrégé

A computer-implemented system for identifying a patient for a trial may include at least one processor. The at least one processor may be programmed to receive an indication of a selected trial, the selected trial being associated with a testing status criterion; access a plurality of patient records associated with a patient of a plurality of patients; determine, using a machine learning model and based on unstructured information from one at least one of the patient records, a likelihood of an occurrence of genomic testing for the patient; determine a genomic testing status of the patient based on the determined likelihood of the occurrence of genomic testing; determine that the genomic testing status satisfies the testing status criterion; and include the patient in a subset of the plurality of patients based on the genomic testing status satisfying the testing status criterion.

Classes IPC  ?

• G16B 40/00 - TIC spécialement adaptées aux biostatistiques; TIC 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
• G16B 45/00 - TIC spécialement adaptées à la visualisation de données liées à la bio-informatique, p. ex. affichage de cartes ou de réseaux
• G16B 50/00 - TIC pour la programmation d’outils ou de systèmes de bases de données spécialement adaptées à la bio-informatique
• 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

collection and systematization of information into computer databases; collection and compilation of information into computer databases in the fields of oncology, medicine, life science and healthcare; database management; electronic data collection and data submission services for business purposes in the fields of oncology, medicine, life science and healthcare; business consulting and management in the field of patient data, namely, management and compilation of computerized databases in the field of oncology patient data for business purposes; providing an on-line database providing business intelligence in the fields of oncology, medicine, life science and healthcare data mining; cloud computing featuring software for use in aggregating, integrating, analyzing, standardizing, and managing clinical data, financial data, electronic medical records data, medical practice management data and billing data in real time from heterogeneous data sources, and for database management in the fields of oncology, medicine, life science and healthcare; cloud computing featuring software for use in analyzing business and clinical intelligence data in the fields of fields of oncology, medicine, life science and healthcare; compiling data for research purposes in the fields of oncology, medicine, life science and healthcare; development, updating and maintenance of software and data bases in the fields of oncology, medicine, life science and healthcare; design of information graphics and data visualization materials; data conversion of electronic information; computer services, namely, providing temporary use of non-downloadable on-line software modules for data management, processing data, data analysis and data visualization

Abrégé

A computer-implemented system for determining a genomic testing status of a patient may include at least one processor programmed receive, from a source, unstructured information from a plurality of patient records associated with a patient; determine, using a first machine learning model, a primary patient record from among the plurality of patient records, wherein at least a portion of information represented in the primary patient record correlates to genomic testing; determine, using a second machine learning model and based on unstructured information from one at least one of the patient records, a likelihood of an occurrence of genomic testing for the patient; determine a genomic testing status of the patient based on the determined likelihood of the occurrence of genomic testing; and display a user interface comprising an indicator of the genomic testing status of the patient and a link to the primary patient record.

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 50/20 - TIC spécialement adaptées au diagnostic médical, à la simulation médicale ou à l’extraction de données médicales; TIC 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 computer-implemented system for determining a genomic testing status of a patient may include at least one processor programmed receive, from a source, unstructured information from a plurality of patient records associated with a patient; determine, using a first machine learning model, a primary patient record from among the plurality of patient records, wherein at least a portion of information represented in the primary patient record correlates to genomic testing; determine, using a second machine learning model and based on unstructured information from one at least one of the patient records, a likelihood of an occurrence of genomic testing for the patient; determine a genomic testing status of the patient based on the determined likelihood of the occurrence of genomic testing; and display a user interface comprising an indicator of the genomic testing status of the patient and a link to the primary patient record.

Classes IPC  ?

• G16B 40/00 - TIC spécialement adaptées aux biostatistiques; TIC 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 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 45/00 - TIC spécialement adaptées à la visualisation de données liées à la bio-informatique, p. ex. affichage de cartes ou de réseaux
• G16B 50/00 - TIC pour la programmation d’outils ou de systèmes de bases de données spécialement adaptées à la bio-informatique

Abrégé

A model-assisted selection system for predicting a date of an event relating to a patient may include at least one processor configured to obtain a medical record including a plurality of unstructured documents and obtain a model for predicting the date of the event. The at least one processor may further be configured to input the medical record into the model and assign, for each of the plurality of unstructured documents, a label from the model among a pre-event label, a mid-event label, a post-event label, and a non-event label. The at least one processor may also be configured to predict a start date of the event based on the labels of the plurality of unstructured documents and output the predicted start date.

Classes IPC  ?

• G16H 50/70 - TIC spécialement adaptées au diagnostic médical, à la simulation médicale ou à l’extraction de données médicales; TIC 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
• G06N 20/00 - Apprentissage automatique
• G06N 5/04 - Modèles d’inférence ou de raisonnement
• 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
• G06F 40/166 - Traitement de texte Édition, p.ex. insertion ou suppression
• G06F 40/289 - Analyse syntagmatique, p.ex. techniques d’états finis ou regroupement

Produits et services

Consulting in the field of life sciences research, namely, supporting custom observational evidence generation, and providing end-to-end services in the nature of scoping support to understand research objectives and how to leverage real world data, identification of targeted patient cohorts, derivation of variables in the nature of outcomes, events, treatments, disease characteristics, patient characteristics, and procedures, curation of custom data and analytic deliverables, and organizational support related to post-delivery publication and communication of findings

Abrégé

A model-assisted system for determining probabilities associated with a patient attribute. The processor may be programmed to access a database storing an unstructured medical record associated with a patient and analyze the medical record to identify snippets of information associated with the patient attribute. The processor may generate, based on each snippet, a snippet vector comprising a plurality of snippet vector elements comprising weight values associated with at least one word included in the snippet. The processor may analyze the snippet vectors to generate a summary vector comprising a plurality of summary vector elements, wherein each of the plurality of summary vector elements is associated with a corresponding snippet vector element and is determined based on an analysis of the corresponding snippet vector element. The processor may further generate, based on the summary vector, at least one output indicative of a probability associated with the patient attribute.

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 50/30 - TIC spécialement adaptées au diagnostic médical, à la simulation médicale ou à l’extraction de données médicales; TIC 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
• G06N 3/044 - Réseaux récurrents, p.ex. réseaux de Hopfield
• G06N 7/01 - Modèles graphiques probabilistes, p.ex. réseaux probabilistes

Abrégé

A model-assisted system for determining probabilities associated with a patient attribute. The processor may be programmed to access a database storing an unstructured medical record associated with a patient and analyze the medical record to identify snippets of information associated with the patient attribute. The processor may generate, based on each snippet, a snippet vector comprising a plurality of snippet vector elements comprising weight values associated with at least one word included in the snippet. The processor may analyze the snippet vectors to generate a summary vector comprising a plurality of summary vector elements, wherein each of the plurality of summary vector elements is associated with a corresponding snippet vector element and is determined based on an analysis of the corresponding snippet vector element. The processor may further generate, based on the summary vector, at least one output indicative of a probability associated with the patient attribute.

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 50/20 - TIC spécialement adaptées au diagnostic médical, à la simulation médicale ou à l’extraction de données médicales; TIC 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 50/70 - TIC spécialement adaptées au diagnostic médical, à la simulation médicale ou à l’extraction de données médicales; TIC 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 computer application software for medical use, namely, featuring software tools for supporting providers, so providers can select therapies in line with best practices, and for identifying potentially relevant studies and trials, and measuring variation in care across a practice or health system; Downloadable computer software, namely, software development tools for streamlining prior medical authorization and reporting pathways compliance to payers

Produits et services

Platform as a service (PAAS) featuring computer software platforms and software tools for use in analyzing real world clinical data, for ensuring data quality, for modeling outcomes, for visualizing data, for use in research in the fields of oncology, medicine, and healthcare; Cloud computing featuring software tools for use in compiling data for research purposes in the field of oncology, medicine, and healthcare

Produits et services

Cloud computing featuring software tools for data analysis in the field of healthcare and oncology; Software as a service (SAAS) services featuring software for cancer centers and other medical treatment centers to identify variability in care and support quality improvement initiatives

Abrégé

A system for mining trial information from electronic medical records may include a processor programmed to analyze electronic medical records (EMRs) to determine first patients associated with a trial and a first trial status during a time period; analyze the EMRs to determine second patients associated with the trial and a second trial status during the time period; analyze the EMRs to determine third patients associated with a status change during the time period. The status change includes a change from a pre-screening status to the first trial status during the time period. The at least one processor may also be programmed to cause a display to display a graphical user interface configured to display a representation of a number of the first patients, a representation of a number of the second patients, and a representation of a number of the third patients.

Classes IPC  ?

• G16H 50/70 - TIC spécialement adaptées au diagnostic médical, à la simulation médicale ou à l’extraction de données médicales; TIC 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 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
• A61B 5/00 - Mesure servant à établir un diagnostic ; Identification des individus

Abrégé

A system for mining trial information from electronic medical records may include a processor programmed to analyze electronic medical records (EMRs) to determine first patients associated with a trial and a first trial status during a time period; analyze the EMRs to determine second patients associated with the trial and a second trial status during the time period; analyze the EMRs to determine third patients associated with a status change during the time period. The status change includes a change from a pre-screening status to the first trial status during the time period. The at least one processor may also be programmed to cause a display to display a graphical user interface configured to display a representation of a number of the first patients, a representation of a number of the second patients, and a representation of a number of the third patients.

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

Abrégé

A computer-implemented system for determining trials using a metastatic condition of a patient may include at least one processor programmed to receive a selection of a patient; access, in response to the selection of the patient, a patient dataset associated with the patient; receive a predicted metastatic condition associated with the patient; cause display of at least a first portion of the patient dataset and the predicted metastatic condition; determine, based on at least a second portion of the patient dataset or the predicted metastatic condition, a subset of trials for the patient, wherein the subset of trials for the patient is determined from a plurality of trials; and cause display of at least the subset of the trials for the patient.

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
• G16H 50/20 - TIC spécialement adaptées au diagnostic médical, à la simulation médicale ou à l’extraction de données médicales; TIC 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 computer-implemented system for determining trials using a metastatic condition of a patient may include at least one processor programmed to receive a selection of a patient; access, in response to the selection of the patient, a patient dataset associated with the patient; receive a predicted metastatic condition associated with the patient; cause display of at least a first portion of the patient dataset and the predicted metastatic condition; determine, based on at least a second portion of the patient dataset or the predicted metastatic condition, a subset of trials for the patient, wherein the subset of trials for the patient is determined from a plurality of trials; and cause display of at least the subset of the trials for the patient.

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 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

Produits et services

Downloadable mobile software for use in maintaining, sharing and managing medical information

Produits et services

Collection and compilation of information into computer databases in the field of medicine and healthcare; collection and systematization of information into computer databases; database management; electronic data collection and data submission services for business purposes in the fields of medicine and healthcare Providing temporary use of on-line non-downloadable cloud computing software for use in connection with presenting data for care coordinators, social workers and care managers related to risk identification, prioritization of treatment and patient management workflows; providing temporary use of non-downloadable computer software for use in connection to manage reporting and alerts for care coordinators, social workers and care managers

Abrégé

A model-assisted system for predicting survivability of a patient may include at least one processor. The processor may be programmed to access a database storing a medical record for the patient. The medical record may include at least one of structured and unstructured information relative to the patient and may lack a structured patient ECOG score. The processor may be further programmed to analyze at least one of the structured and unstructured information relative to the patient; based on the analysis, and in the absence of a structured ECOG score, generate a performance status prediction for the patient; and provide an output indicative of the predicted performance status. The analysis of at least one of the structured and unstructured information and the generation of the predicted performance status may be performed by at least one of a trained machine learning model or a natural language processing algorithm.

Classes IPC  ?

• G16H 50/30 - TIC spécialement adaptées au diagnostic médical, à la simulation médicale ou à l’extraction de données médicales; TIC 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

Abrégé

A model-assisted system for predicting survivability of a patient may include at least one processor. The processor may be programmed to access a database storing a medical record for the patient. The medical record may include at least one of structured and unstructured information relative to the patient and may lack a structured patient ECOG score. The processor may be further programmed to analyze at least one of the structured and unstructured information relative to the patient; based on the analysis, and in the absence of a structured ECOG score, generate a performance status prediction for the patient; and provide an output indicative of the predicted performance status. The analysis of at least one of the structured and unstructured information and the generation of the predicted performance status may be performed by at least one of a trained machine learning model or a natural language processing algorithm.

Classes IPC  ?

• G16H 50/50 - TIC spécialement adaptées au diagnostic médical, à la simulation médicale ou à l’extraction de données médicales; TIC 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 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 50/70 - TIC spécialement adaptées au diagnostic médical, à la simulation médicale ou à l’extraction de données médicales; TIC 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 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
• G06N 5/04 - Modèles d’inférence ou de raisonnement
• G06N 20/00 - Apprentissage automatique
• G06N 5/02 - Représentation de la connaissance; Représentation symbolique

Abrégé

A graphical user interface for displaying an electronic medical record associated with a patient is provided. The graphical user interface may include an area configured to display patient information, which may include at least a name of the patient. The graphical user interface may also include an indicator displayed in association with the name of the patient. The indicator may include information specifying that the patient is potentially eligible for one or more trials, the patient is participating in one or more trials, or the patient has completed one or more trials.

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
• G16H 15/00 - TIC spécialement adaptées aux rapports médicaux, p.ex. leur création ou leur transmission
• G06F 3/0482 - Interaction avec des listes d’éléments sélectionnables, p.ex. des menus

Abrégé

A system for providing guideline concordance may include at least one processing device programmed to receive, via a graphical user interface of a user device, a search term associated with a drug; access a structured database to identify, based on the search term, a description of at least one regimen that includes the search term; display, via the graphical user interface, a selectable identifier of the at least one regimen; receive, via the graphical user interface, a selection of a regimen, wherein the regimen is associated with the drug; generate, based on the structured database, one or more indications that are concordant for the regimen; receive, via the graphical user interface, a selection of a concordant indication; and store, in an electronic health record database, a patient record with information identifying the selected regimen and the selected indication.

Classes IPC  ?

• G16H 70/20 - TIC spécialement adaptées au maniement ou au traitement de références médicales concernant des pratiques ou des directives
• 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
• 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

A system for providing guideline concordance may include at least one processing device programmed to receive, via a graphical user interface of a user device, a search term associated with a drug; access a structured database to identify, based on the search term, a description of at least one regimen that includes the search term; display, via the graphical user interface, a selectable identifier of the at least one regimen; receive, via the graphical user interface, a selection of a regimen, wherein the regimen is associated with the drug; generate, based on the structured database, one or more indications that are concordant for the regimen; receive, via the graphical user interface, a selection of a concordant indication; and store, in an electronic health record database, a patient record with information identifying the selected regimen and the selected indication.

Classes IPC  ?

• G16H 70/20 - TIC spécialement adaptées au maniement ou au traitement de références médicales concernant des pratiques ou des directives
• 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
• 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 16/338 - Présentation des résultats des requêtes
• G06F 3/048 - Techniques d’interaction fondées sur les interfaces utilisateur graphiques [GUI]

Abrégé

A system for tracking adverse events may include at least one processing device programmed to receive a request from a user to record an adverse event experienced by a patient; receive a search term input by the user; identify, in an adverse event database and based on the search term, at least one database record for an adverse event, wherein the at least one database record includes an adverse event type and at least one characteristic; receive, via an input field, a rating of the at least one characteristic for the patient; generate an adverse event record based on the adverse event type and the rating; and store the adverse event record in an adverse event log.

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 16/2457 - Traitement des requêtes avec adaptation aux besoins de l’utilisateur
• H04L 9/32 - Dispositions pour les communications secrètes ou protégées; Protocoles réseaux de sécurité comprenant des moyens pour vérifier l'identité ou l'autorisation d'un utilisateur du système
• G16H 15/00 - TIC spécialement adaptées aux rapports médicaux, p.ex. leur création ou leur transmission
• 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
• G06F 16/248 - Présentation des résultats de requêtes

Abrégé

A computer-implemented system for managing electronic medical records may include one or more processors configured to receive, via a user interface of a user device, a user input for adding a new trial and create a new trial portfolio based on the received user input. The portfolio may comprise patient eligibility criteria associated with the new trial. The one or more processors may also be configured to automatically create a patient-trial matching algorithm for the new trial based on the trial eligibility criteria and determine, based on electronic patient medical records associated with a plurality of patients and the patient-trial matching algorithm, at least one suggested patient determined to be eligible for the new trial. The one or more processors may further be configured to transmit, to the user device, instructions for displaying information representing the at least one suggested patient in the user interface.

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

A model-assisted system for identifying candidates for a cohort based on a biomarker may include at least one processor. The processor may be programmed to access a database from which information associated with a population of individuals can be derived; provide, to a generalized biomarker model, a first biomarker associated with a cohort, the generalized biomarker model being trained based on one or more second biomarkers using the information, wherein the first biomarker is different from the one or more second biomarkers; receive, from the generalized biomarker model, a first output indicating a first group of the population of individuals exceeding a first likelihood threshold of having been tested for the first biomarker; and determine, based on the first output, whether an individual from among the first group of the population of individuals is a candidate for the cohort.

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

A model-assisted selection system for predicting a date of an event relating to a patient may include at least one processor configured to obtain a medical record including a plurality of unstructured documents and obtain a model for predicting the date of the event. The at least one processor may further be configured to input the medical record into the model and assign, for each of the plurality of unstructured documents, a label from the model among a pre-event label, a mid-event label, a post-event label, and a non-event label. The at least one processor may also be configured to predict a start date of the event based on the labels of the plurality of unstructured documents and output the predicted start date.

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

Systems and methods are disclosed for monitoring models for bias. In one implementation, a system for automatically assessing a deployed model for selection of a cohort may include a processing device programmed to: apply the deployed model to data representing a first plurality of individuals, the data including at least one characteristic of the first plurality of individuals; based on the application, select a subset of the first plurality of individuals as a cohort; receive data representing a second plurality of individuals labeled as within the cohort, the data including the at least one characteristic of the second plurality of individuals; compare the selected subset and the second plurality of individuals along the at least one characteristic; and determine whether the comparison results in a difference between the selected subset and the second plurality of individuals greater than a threshold.

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
• G06N 20/00 - Apprentissage automatique

Abrégé

Systems and methods are disclosed for monitoring models for bias. In one implementation, a system for automatically assessing a deployed model for selection of a cohort may include a processing device programmed to: apply the deployed model to data representing a first plurality of individuals, the data including at least one characteristic of the first plurality of individuals; based on the application, select a subset of the first plurality of individuals as a cohort; receive data representing a second plurality of individuals labeled as within the cohort, the data including the at least one characteristic of the second plurality of individuals; compare the selected subset and the second plurality of individuals along the at least one characteristic; and determine whether the comparison results in a difference between the selected subset and the second plurality of individuals greater than a threshold.

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
• G06N 20/00 - Apprentissage automatique

Produits et services

Collection, systematization and compilation of information into computer databases in the fields of medicine and healthcare; database management in the fields of medicine and healthcare; electronic data collection and data submission services for business purposes in the fields of medicine and healthcare; business consulting and management in the field of clinical trials, namely, management and compilation of computerized databases for business purposes in the field of clinical trials. Data mining in the fields of medicine and healthcare; cloud computing for ensuring data quality in the fields of medicine and healthcare; cloud computing for modeling outcomes in the fields of medicine and healthcare; cloud computing for visualizing data in the fields of medicine and healthcare; cloud computing for database management in the fields of medicine and healthcare; database design and development in the fields of medicine and healthcare; compiling data for research purposes in the fields of medical science, healthcare quality improvement and medical consultancy; design, development, installation and maintenance of computer software in the fields of medicine and healthcare; development, updating and maintenance of software and databases in the fields of medicine and healthcare; design of information graphics and data visualization materials in the fields of medicine and healthcare; data conversion of electronic information in the fields of medicine and healthcare.

Abrégé

Systems and methods are disclosed for selecting cohorts. In one implementation, a model-assisted selection system for identifying candidates for placement into a cohort includes a data interface and at least one processing device. The at least one processing device is programmed to access, via the data interface, a database from which feature vectors associated with an individual from among a population of individuals can be derived; derive, for the individual, one or more feature vectors from the database; provide the one or more feature vectors to a model; receive an output from the model; and determine whether the individual from among the population of individuals is a candidate for the cohort based on the output received from the model.

Classes IPC  ?

• G06N 20/00 - Apprentissage automatique
• G06N 5/046 - Inférence en avant; Systèmes de production
• 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 50/70 - TIC spécialement adaptées au diagnostic médical, à la simulation médicale ou à l’extraction de données médicales; TIC 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

Collection and compilation of information into computer databases in the field of medicine and healthcare; collection and systematization of information into computer databases; database management; electronic data collection and data submission services for business purposes in the fields of medicine and healthcare; business consulting and management in the field of clinical trials, namely, management and compilation of computerized databases in the field of clinical trials for business purposes; collection and compilation of regulatory grade data non-downloadable computer software for use in extracting, analyzing, standardizing and cleaning clinical, financial, and operational data, for ensuring data quality, for modeling outcomes, for visualizing data, and for database management in the fields of medicine and healthcare; non-downloadable computer software for use as a patient portal; non-downloadable computer software to facilitate access to health information for patients and providers; non-downloadable computer software for use in facilitating access to disease-specific data sets and research project information; non-downloadable computer software for medical research proposal submissions; non-downloadable computer software for use in facilitating access to healthcare data, metrics, analytics, and quality information; non-downloadable computer software for use as a dashboard for clinicians; data mining; cloud computing featuring software for use in extracting, analyzing, standardizing and cleaning clinical, financial, and operational data, for ensuring data quality, for modeling outcomes, for visualizing data, and for database management in the fields of medicine and healthcare; database design and development; compiling data for research purposes in the field of medical science and medical consultancy; design and development of computer software; design, development, installation and maintenance of computer software; development, updating and maintenance of software and data bases; design of information graphics and data visualization materials; data conversion of electronic information; design and development of computer software for use as an online patient portal; design and development of computer software to facilitate access to health information for patients and providers; design and development of computer software for use in facilitating access to disease-specific data sets and research project information; design and development of computer software for medical research proposal submissions; design and development of computer software for use in facilitating access to healthcare data, metrics, analytics, and quality information; design and development of computer software for use as a dashboard for clinicians

Abrégé

Systems and methods are disclosed for selecting cohorts. In one implementation, a model-assisted selection system for identifying candidates for placement into a cohort includes a data interface and at least one processing device. The at least one processing device is programmed to access, via the data interface, a database from which feature vectors associated with an individual from among a population of individuals can be derived; derive, for the individual, one or more feature vectors from the database; provide the one or more feature vectors to a model; receive an output from the model; and determine whether the individual from among the population of individuals is a candidate for the cohort based on the output received from the model.

Classes IPC  ?

• G06F 15/18 - dans lesquels un programme est modifié en fonction de l'expérience acquise par le calculateur lui-même au cours d'un cycle complet; Machines capables de s'instruire (systèmes de commande adaptatifs G05B 13/00;intelligence artificielle G06N)
• G06N 5/04 - Modèles d’inférence ou de raisonnement
• 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
• G06K 9/66 - Méthodes ou dispositions pour la reconnaissance utilisant des moyens électroniques utilisant des comparaisons ou corrélations simultanées de signaux images avec une pluralité de références, p.ex. matrice de résistances avec des références réglables par une méthode adaptative, p.ex. en s'instruisant
• G06K 9/00 - Méthodes ou dispositions pour la lecture ou la reconnaissance de caractères imprimés ou écrits ou pour la reconnaissance de formes, p.ex. d'empreintes digitales
• G06N 20/00 - Apprentissage automatique
• G16H 50/70 - TIC spécialement adaptées au diagnostic médical, à la simulation médicale ou à l’extraction de données médicales; TIC 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

Systems and methods are disclosed for selecting cohorts. In one implementation, a model-assisted selection system for identifying candidates for placement into a cohort includes a data interface and at least one processing device. The at least one processing device is programmed to access, via the data interface, a database from which feature vectors associated with an individual from among a population of individuals can be derived; derive, for the individual, one or more feature vectors from the database; provide the one or more feature vectors to a model; receive an output from the model; and determine whether the individual from among the population of individuals is a candidate for the cohort based on the output received from the model.

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 50/70 - TIC spécialement adaptées au diagnostic médical, à la simulation médicale ou à l’extraction de données médicales; TIC 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

Systems and methods are disclosed for visualizing medical data. In one implementation, the systems each comprise a database, a memory that stores a set of instructions and at least one processor in communication with the memory configured to execute the set of instructions so the system may receive the medical data in one or more formats from a plurality of sources, the medical data comprising a plurality of events associated with one or more patients, convert the medical data from the one or more formats to a standardized data format, store the standardized data in the database, receive a query comprising at least one patient characteristic, query the database to identify a patient associated with the at least one patient characteristic, generate a graphical user interface to include the standardized data represented as a timeline of events associated with the identified patient and display the generated graphical user interface.

Classes IPC  ?

• G16H 10/65 - 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 stockées sur des supports d’enregistrement portables, p.ex. des cartes à puce, des étiquettes d’identification radio-fréquence [RFID] ou des CD
• G16H 40/63 - TIC spécialement adaptées à la gestion ou à l’administration de ressources ou d’établissements de santé; TIC 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 local
• G06F 16/248 - Présentation des résultats de requêtes
• G06F 16/9535 - Adaptation de la recherche basée sur les profils des utilisateurs et la personnalisation

Produits et services

Collection and compilation of information into computer databases in the field of medicine and healthcare; collection and systematisation of information into computer databases; database management; electronic data collection and data submission services for business purposes in the fields of medicine and healthcare; business consulting and management in the field of clinical trials, namely, management and compilation of computerized databases in the field of clinical trials for business purposes Data mining; cloud computing featuring software for use in extracting, analyzing, standardizing and cleaning clinical, financial, and operational data, for ensuring data quality, for modeling outcomes, for visualizing data, and for database management in the fields of medicine and healthcare; database design and development; compiling data for research purposes in the field of medical science and medical consultancy; design and development of computer software; design, development, installation and maintenance of computer software; development, updating and maintenance of software and data bases; design of information graphics and data visualization materials; data conversion of electronic information

Abrégé

Methods, systems, and apparatus, including computer programs encoded on computer storage media, to present a video. One of the methods includes obtaining one or more unstructured documents. The method includes obtaining, by a computer system, a data model, the data model identifying a type of fact that can be determined from the one or more unstructured documents. The method includes determining, by the computer system, a channel to extract facts from the document based on the type of fact. The method includes distributing, by the computer system, the one or more unstructured documents to the channel. The method includes extracting, by the channel, facts from the one or more unstructured documents. The method also includes storing the facts in a data model.

Classes IPC  ?

• G06N 20/00 - Apprentissage automatique
• G16H 50/70 - TIC spécialement adaptées au diagnostic médical, à la simulation médicale ou à l’extraction de données médicales; TIC 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

Management and compilation of computerized databases; Electronic data collection and data submission services for business purposes in the fields of oncology, medicine, life science and healthcare; Collection and compilation of information into computer databases in the fields of oncology, medicine, life science and healthcare; Business consulting and management in the field of patient data, namely, management and compilation of computerized databases in the field of oncology patient data for business purposes; Providing an on-line database providing business intelligence in the fields of oncology, medicine, life science and healthcare; Managing databases for the design and administration of scientific and medical research projects, and of clinical trials for pharmaceuticals, medical devices and medical procedures; Managing databases for data collection, data sharing, information exchange, and collaboration regarding scientific and medical research projects, and regarding clinical trials for pharmaceuticals, medical devices and medical procedures Data mining; Providing temporary use of on-line non-downloadable cloud computing software for in aggregating, integrating, analyzing, standardizing, managing and reporting of data in clinical trials, clinical data, financial data, electronic medical records data, medical practice management data and billing data in real time from heterogeneous data sources, and for database management in the fields of oncology, medicine, life science and healthcare; Providing temporary use of on-line non-downloadable cloud computing software for use in data collection, data sharing, information exchange, and collaboration regarding scientific and medical research projects, and regarding clinical trials for pharmaceuticals, medical devices and medical procedures; Providing temporary use of on-line non-downloadable cloud computing software for use in analyzing business and clinical intelligence data in the fields of fields of oncology, medicine, life science and healthcare; Providing temporary use of on-line non-downloadable cloud computing software for data compilation for research purposes in the fields of oncology, medicine, life science and healthcare; Development, updating and maintenance of software and databases in the fields of oncology, medicine, life science and healthcare; Providing temporary use of on-line non-downloadable cloud computing software for use in design and administration of scientific and medical research projects, and of clinical trials for pharmaceuticals, medical devices and medical procedures; Providing temporary use of on-line non-downloadable cloud computing software for the tracking and management of patients, schedules, and performance milestones and metrics involved in scientific and medical research, and in clinical trials for pharmaceuticals, medical devices and medical procedures; Application service provider, namely, hosting, managing, developing, and maintaining web sites, software, applications, and databases for data collection, data sharing, information exchange, and collaboration regarding scientific and medical research projects, and regarding clinical trials for pharmaceuticals, medical devices and medical procedures; Application service provider, namely, hosting, managing, developing, and maintaining web sites, software, and applications for the design and administration of scientific and medical research projects, and of clinical trials for pharmaceuticals, medical devices and medical procedures; Application service provider, namely, hosting, managing, developing, and maintaining web sites, software, and applications for the tracking and management of patients, schedules, and performance milestones and metrics involved in scientific and medical research, and in clinical trials for pharmaceuticals, medical devices and medical procedures

Produits et services

Providing temporary use of on-line non-downloadable cloud computing software for the monitoring and reporting of regulatory, standards, and safety compliance in scientific and medical research, and in clinical trials for pharmaceuticals, medical devices and medical procedures; application service provider, namely, hosting, managing, developing, and maintaining web sites, software, and applications for the monitoring and reporting of regulatory, standards, and safety compliance in scientific and medical research, and in clinical trials for pharmaceuticals, medical devices and medical procedures

Produits et services

Collection and systematization of information into computer databases; Collection and compilation of information into computer databases in the fields of oncology, medicine, life science and healthcare; Database management; Electronic data collection and data submission services for business purposes in the fields of oncology, medicine, life science and healthcare; Business consulting and management in the field of patient data, namely, management and compilation of computerized databases in the field of oncology patient data for business purposes; Providing an on-line database providing business intelligence in the fields of oncology, medicine, life science and healthcare Data mining; Cloud computing featuring software for use in aggregating, integrating, analyzing, standardizing, and managing clinical data, financial data, electronic medical records data, medical practice management data and billing data in real time from heterogeneous data sources, and for database management in the fields of oncology, medicine, life science and healthcare; Cloud computing featuring software for use in analyzing business and clinical intelligence data in the fields of fields of oncology, medicine, life science and healthcare; Compiling data for research purposes in the fields of oncology, medicine, life science and healthcare; Development, updating and maintenance of software and data bases in the fields of oncology, medicine, life science and healthcare; Design of information graphics and data visualization materials; Data conversion of electronic information; Computer services, namely, providing temporary use of non-downloadable on-line software modules for data management, processing data, data analysis and data visualization

Produits et services

Collection and compilation of information into computer databases in the field of medicine and healthcare; collection and systematization of information into computer databases; database management; electronic data collection and data submission services for business purposes in the fields of medicine and healthcare; business consulting and management in the field of clinical trials, namely, management and compilation of computerized databases in the field of clinical trials for business purposes Data mining; cloud computing featuring software for use in extracting, analyzing, standardizing and cleaning clinical, financial, and operational data, for ensuring data quality, for modeling outcomes, for visualizing data, and for database management in the fields of medicine and healthcare; database design and development; compiling data for research purposes in the field of medical science and medical consultancy; design and development of computer software; design, development, installation and maintenance of computer software; development, updating and maintenance of software and data bases; design of information graphics and data visualization materials; data conversion of electronic information

Produits et services

Collection and compilation of information into computer databases in the field of medicine and healthcare; collection and systematisation of information into computer databases; database management; electronic data collection and data submission services for business purposes in the fields of medicine and healthcare; business consulting and management in the field of clinical trials, namely, management and compilation of computerized databases in the field of clinical trials for business purposes Data mining; cloud computing featuring software for use in extracting, analyzing, standardizing and cleaning clinical, financial, and operational data, for ensuring data quality, for modeling outcomes, for visualizing data, and for database management in the fields of medicine and healthcare; database design and development; compiling data for research purposes in the field of medical science and medical consultancy; design and development of computer software; design, development, installation and maintenance of computer software; development, updating and maintenance of software and data bases; design of information graphics and data visualization materials; data conversion of electronic information

Produits et services

online non-downloadable software for use in maintaining, sharing and managing medical information; online non-downloadable software for use in accessing, viewing and displaying medical history and appointments; online non-downloadable software for use in accessing, viewing and displaying medical records, medical charts, medical test results, laboratory results, radiology results, pathology results, CAT scan results, medical treatment and diagnosis information, medical prescriptions, and patient vital statistics websites and web portals for accessing, viewing and displaying patient medical history and appointments; websites and web portals for accessing, viewing and displaying medical records, medical charts, medical test results, laboratory results, radiology results, pathology results, CAT scan results, medical treatment and diagnosis information, medical prescriptions, and patient vital statistics