Methods, systems, and apparatus, including computer programs encoded on computer storage media, for generating data structures from graphs. The computer accesses a graph having patient nodes representing patients and patient health data nodes representing health data for patients, the nodes being connected by edges. The computer generates subgraphs by identifying patient nodes and patient health data nodes associated with a particular healthcare provider. The computer generates, by a subgraph neural network, a healthcare provider data structure for a respective subgraph. The computer generates, by a first graph neural network, patient data structures for a respective patient graph network and health data structures for a respective health data graph network. Each healthcare provider data structure, patient graph network, health data structure, has a lower dimension than the corresponding subgraph, patient graph network, and health data graph network, respectively. The computer provides at least one of the data structures to a model.
G16H 50/20 - ICT specially adapted for medical diagnosis, medical simulation or medical data miningICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for computer-aided diagnosis, e.g. based on medical expert systems
G16H 50/70 - ICT specially adapted for medical diagnosis, medical simulation or medical data miningICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for mining of medical data, e.g. analysing previous cases of other patients
Methods, systems, and apparatus, including computer programs encoded on computer storage media, for managing a life cycle of a label. In some implementations, a request for generating a label for a product can be received. Workflows for generation of the label can be identified. The workflows for the generation of the label can be executed. In response to executing the workflows for the generation of the label, data indicative of the generation of the label can be submitted to a health authority. Data indicative of the approval of the label for the product can be received from the health authority. In response to receiving data from the health authority indicative of approval of the label for the product, a layout for the label can be generated for the product.
G16H 40/20 - ICT specially adapted for the management or administration of healthcare resources or facilitiesICT specially adapted for the management or operation of medical equipment or devices for the management or administration of healthcare resources or facilities, e.g. managing hospital staff or surgery rooms
A method includes determining, for one or more variables that describe a potential subject for a study, corresponding functions representing a relationship between the one or more variables and (i) an estimated burden or (ii) an estimated burden reduction that would be imposed on the potential subject by a protocol of the study if the potential subject were to participate in the study. The estimated burden or the estimated burden reduction is dependent on subject-specific values for the one or more variables absent participation of the potential subject in the study. The method includes using the corresponding functions to determine, for each individual of a plurality of individuals, a set of estimated burden values or estimated burden reduction values associated with the one or more variables. The method also includes identifying a subset of the plurality of individuals or a patient profile to be prioritized for recruitment for the study.
G16H 10/20 - ICT specially adapted for the handling or processing of patient-related medical or healthcare data for electronic clinical trials or questionnaires
G16H 10/60 - ICT specially adapted for the handling or processing of patient-related medical or healthcare data for patient-specific data, e.g. for electronic patient records
4.
MANAGEMENT AND TRACKING SOLUTION FOR SPECIFIC PATIENT CONSENT ATTRIBUTES AND PERMISSIONS
A method of managing consent using a computing device, the consent is given by a subject to one or more events in one or more studies, wherein the consent and the plurality of activities are changeable, the method including: authoring one or more first data forms describing the one or more events and one or more selections responsive to the one or more events; authoring, for each of the plurality of subjects, one or more second data forms including description of a plurality of preferences; forming, for a first of the plurality of subjects, an Informed Consent Forms document by combining the one or more first data forms of a first of the one or more studies and one or more second data forms for the first subject; and generating a manifest indicating the one or more events in the first study to which the first subject has granted consent.
G16H 10/20 - ICT specially adapted for the handling or processing of patient-related medical or healthcare data for electronic clinical trials or questionnaires
G06F 3/0482 - Interaction with lists of selectable items, e.g. menus
System and method to determine a reduced cohort criteria, the method including: defining N selection criteria to select a cohort from among a universe of patient data; querying a patient database, by use of a processor, and by use of the N selection criteria, in order to define a full patient population; selecting a subset of size M of the N selection criteria, to produce a subset criteria; selecting a permutation of the subset criteria, to produce a permuted subset criteria in a predetermined order; for each member of the permuted subset criteria: querying the patient database by use of the member of the permuted subset criteria to produce a respective interim patient population; combining all respective interim patient populations to produce a partial patient population; and calculating, by a processor, a coverage figure of merit that compares the partial patient population to the full patient population.
G16H 10/20 - ICT specially adapted for the handling or processing of patient-related medical or healthcare data for electronic clinical trials or questionnaires
G16Z 99/00 - Subject matter not provided for in other main groups of this subclass
6.
PATIENT PRIVACY COMPLIANT TARGETING SYSTEM AND METHOD
A method includes receiving data and integrating the data into a computing system. The method also includes applying a machine learning system to identify patients from the integrated data to place in one or more communities that include consumer-related data and social determinants of health data. The method also includes combining path projection, aggregation, and embedding to establish one or more paths to connect the patients to the communities based on the consumer-related data and/or the social determinants of health data in the one or more communities. The method also includes training a machine learning system to identify a correct path among the one or more established paths to place the patients on to be connected to the one or more communities.
G16H 50/70 - ICT specially adapted for medical diagnosis, medical simulation or medical data miningICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for mining of medical data, e.g. analysing previous cases of other patients
A computer-implemented method includes a machine learning system receiving distinct types of data associated with multiple individual entities. For each of the individual entities, the machine learning system determines a first attribute that indicates a predicted attribute of the entity based on analysis of the data. The machine learning system also determines a second attribute that indicates a predicted quality attribute of the entity, based on analysis of the data. An attribute weighting module of the machine learning system generates weight values for each of the first attribute and the second attribute of the entity. The machine learning system generates a data structure that identifies a set of entities from among the multiple individual entities, where entities of the set are ranked based on a tier indicator that corresponds to either the first attribute, the second attribute, or both.
A graph-based clinical concept mapping algorithm maps ICD-9 (International Classification of Disease, Revision 9) and ICD-10 (International Classification of Disease, Revision 10) codes to unified Systematized Nomenclature of Medicine (SNOMED) clinical concepts to normalize longitudinal healthcare data to thereby improve tracking and the use of such data for research and commercial purposes. The graph-based clinical concept mapping algorithm advantageously combines a novel graph-based search algorithm and natural language processing to map orphan ICD codes (those without equivalents across codebases) by finding optimally relevant shared SNOMED concepts. The graph-based clinical concept mapping algorithm is further advantageously utilized to group ICD-9/10 codes into higher order, more prevalent SNOMED concepts to support clinical interpretation.
A method and system to provide multi-layered access control for healthcare datasets are disclosed. The method comprises defining an information policy for each of healthcare datasets, wherein the information policy comprises information access permissions. Further, an organization policy is defined for each of the healthcare datasets, wherein the organization policy comprises license permissions for organizations accessing the healthcare datasets. Thereafter, a user account master policy is defined for each of the healthcare datasets, wherein the user account master policy comprises account permissions assigned to users of the organizations. Subsequently, a master user policy is generated for each of the users based on the information policy, the organization policy, the user account master policy, or a combination thereof, wherein the master user policy comprises access control permissions to provide each of the users access to the healthcare datasets.
G06F 21/62 - Protecting access to data via a platform, e.g. using keys or access control rules
G16H 10/60 - ICT specially adapted for the handling or processing of patient-related medical or healthcare data for patient-specific data, e.g. for electronic patient records
A deep learning model implements continuous, lifelong machine learning (LML) based on a Bayesian neural network using an inventive framework including wide, deep, and prior components that employ diverse algorithms to leverage available real-world healthcare data differently to improve prediction performance. The outputs from each component of the framework are fed into a wide and shallow neural network and the posterior structure of the final model output may be utilized as a prior structure when the deep learning model is refreshed with new data in a deep learning process. Lifelong learning is implemented by dynamically integrating present learning from the wide and deep learning components with past learning from traditional tree models in the prior component into future predictions. Thus, the present Bayesian deep neural network-based LML model increases accuracy in identifying patient profiles by continuously learning, as new data become available, without forgetting prior knowledge.
G16H 10/60 - ICT specially adapted for the handling or processing of patient-related medical or healthcare data for patient-specific data, e.g. for electronic patient records
G16H 40/20 - ICT specially adapted for the management or administration of healthcare resources or facilitiesICT specially adapted for the management or operation of medical equipment or devices for the management or administration of healthcare resources or facilities, e.g. managing hospital staff or surgery rooms
G16H 50/70 - ICT specially adapted for medical diagnosis, medical simulation or medical data miningICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for mining of medical data, e.g. analysing previous cases of other patients
G16H 70/20 - ICT specially adapted for the handling or processing of medical references relating to practices or guidelines
G16H 70/40 - ICT specially adapted for the handling or processing of medical references relating to drugs, e.g. their side effects or intended usage
G16H 70/60 - ICT specially adapted for the handling or processing of medical references relating to pathologies
Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for a computing platform that identifies information about a trial program, where the information is related to healthcare data included in datasets, and identifies an investigator based on the information about the trial program. A data analytics model of the platform generates an initial provider score for each provider in a group of providers based on analysis of the information. The analyzed information of the datasets includes healthcare data describing interactions between patients and providers in the group and criteria for the trial program. The platform provides a request to a subset of providers using the initial provider scores. The request is an invitation to for each provider to join a referral network. The platform uses the request to establish referral connections between the trial investigator and a provider in the subset.
G16H 10/60 - ICT specially adapted for the handling or processing of patient-related medical or healthcare data for patient-specific data, e.g. for electronic patient records
G16H 10/20 - ICT specially adapted for the handling or processing of patient-related medical or healthcare data for electronic clinical trials or questionnaires
G16H 50/20 - ICT specially adapted for medical diagnosis, medical simulation or medical data miningICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for computer-aided diagnosis, e.g. based on medical expert systems
G16H 50/70 - ICT specially adapted for medical diagnosis, medical simulation or medical data miningICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for mining of medical data, e.g. analysing previous cases of other patients
G16H 80/00 - ICT specially adapted for facilitating communication between medical practitioners or patients, e.g. for collaborative diagnosis, therapy or health monitoring
Methods, systems, and apparatus, including computer programs encoded on computer storage media, for cross platform communications. In some implementations, a first request is received over a network for an application from a client device. In response, application data is generated that includes a software development kit (SDK) incorporated in the application. Tracking data is generated that comprises (i) data identifying the SDK and (ii) data identifying the client device. The generated application data is provided to the client device, the SDK incorporated in the application enabling the application of the client device to communicate with a different application, wherein the SDK is incorporated in the application responsive to receipt of the first request. A list of applications are identified that have the SDK based on the tracking data. A message is generated and provided to each application on the list.
A method includes ingesting, by a clinical trial management platform, data from multiple clinical trial site systems associated with multiple clinical trials, responsive to authentication of user credentials of a user, providing the user with access to the clinical trial management platform, based on the user credentials, identifying a particular clinical trial associated with the user, through the clinical trial management platform, providing the user with access to multiple clinical trial software services based on the authenticated user credentials, based on the ingested data for the particular clinical trial associated with the user, presenting to the user a user-specific task list for user tasks related to the particular clinical trial; and through the clinical trial management platform, establishing a communication link between the user and another user, the communication link enabling direct, real-time messaging via the clinical trial management platform between the user and the other user.
G16H 10/20 - ICT specially adapted for the handling or processing of patient-related medical or healthcare data for electronic clinical trials or questionnaires
A method includes ingesting, by a clinical trial management platform, data from multiple clinical trial site systems associated with multiple clinical trials, responsive to authentication of user credentials of a user, providing the user with access to the clinical trial management platform, based on the user credentials, identifying a particular clinical trial associated with the user, through the clinical trial management platform, providing the user with access to multiple clinical trial software services based on the authenticated user credentials, based on the ingested data for the particular clinical trial associated with the user, presenting to the user a user-specific task list for user tasks related to the particular clinical trial; and through the clinical trial management platform, establishing a communication link between the user and another user, the communication link enabling direct, real-time messaging via the clinical trial management platform between the user and the other user.
G16H 10/20 - ICT specially adapted for the handling or processing of patient-related medical or healthcare data for electronic clinical trials or questionnaires
G16H 10/60 - ICT specially adapted for the handling or processing of patient-related medical or healthcare data for patient-specific data, e.g. for electronic patient records
G16H 50/70 - ICT specially adapted for medical diagnosis, medical simulation or medical data miningICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for mining of medical data, e.g. analysing previous cases of other patients
G16H 15/00 - ICT specially adapted for medical reports, e.g. generation or transmission thereof
15.
System and method for enhanced distribution of data to compute nodes
A computer-implemented includes a computing system receiving one or more queries. The computing system includes one or more compute nodes that perform computations for determining a response to at least one query. The system stores, in a storage device, domain data that includes at least one of: a dataset, a metric associated with the domain data, a query time, or a usage pattern that is based, in part, on the one or more queries. The method includes the system generating a distribution model based on analysis of the domain data. The distribution model is generated using machine learning logic executed by the system. The method further includes the system using the distribution model to distribute data to the one or more compute nodes. The distributed data is used to determine, within a threshold response time, the response to the at least one query.
Methods, systems, and apparatus for generating synthetic patient data and simulating clinical studies. In one aspect, a method includes obtaining a disease of interest for an in silico clinical study and obtaining historic patient data associated with the disease of interest. The historic patient data includes patient attributes for each patient. The method includes, based on the patient attributes, generating synthetic patient data. The synthetic patient data reproduce statistical properties of the historic patient data. The method includes applying the synthetic patient data to the in silico clinical study configured to predict a clinical study outcome and providing, based on the predicted clinical study outcome, feedback data that specify one or more parameters used in generating the synthetic patient data.
G16H 10/20 - ICT specially adapted for the handling or processing of patient-related medical or healthcare data for electronic clinical trials or questionnaires
09 - Scientific and electric apparatus and instruments
Goods & Services
Downloadable material, reports, and digital content in the pharmaceutical, medical, healthcare, life sciences, and clinical trial industries; Downloadable software for use in the pharmaceutical, medical, healthcare, life sciences, and clinical trial industries; Downloadable software for collaboration, management, and communication in the pharmaceutical, medical, healthcare, life sciences, and clinical trial industries; Downloadable software for data, system, and software integration, aggregation, and access management in the pharmaceutical, medical, healthcare, life sciences, and clinical trial industries; Downloadable software for providing clinical trial, pharmaceutical, medical, healthcare, and life sciences information and analysis; Downloadable software for collecting, managing, and analyzing clinical trial data and information; Downloadable software for accessing, collecting, managing, tracking, analyzing, and reporting data in the pharmaceutical, medical, healthcare, life sciences, and clinical trial industries; Downloadable software for clinical trial patient matching and analysis; Electronic database recorded on computer media containing pharmaceutical, medical, healthcare, and life sciences information; Downloadable database in the field of clinical trials; Downloadable graphical user interface software; Downloadable software; Downloadable reports and material.
42 - Scientific, technological and industrial services, research and design
Goods & Services
Business consulting, administrative, management, and information services in the field of clinical trials; Regulatory submission management, namely, assisting others in preparing and filing applications for new drugs, biologics, and devices with governmental regulatory bodies; Providing consulting services in the field of regulatory submission management, clinical research, and clinical trials; Business consulting and management in the field of clinical trials, namely, clinical data and regulatory submission management on behalf of medical, biopharmaceutical and biotechnology companies to assist them with clinical research, clinical trials and applications for drug, biologics, and device approval; Recruitment services, namely, advertising clinical trials, recruiting patients, and placing patients for participation in clinical trials for testing of drugs, biologics, and devices; Providing a website and online portal featuring business information in the pharmaceutical, medical, healthcare, and life sciences fields; Providing independent review of clinical trials for business purposes; Consulting services in the field of clinical trial patient relationship management; Contract clinical research and contract product development; Database management and data processing services; Business networking and online business networking services; Management and compilation of business directories and registries; Online business directories featuring information on clinical trials, clinical trial professionals, and patients in clinical trials; business services; business platform services Scientific and medical research in the field of clinical trials, pharmaceuticals, biologics, and devices; Scientific and medical research, namely, conducting clinical trials and providing documentation for submission to regulatory authorities in connection therewith; Providing scientific and medical research information in the field of clinical trials, pharmaceuticals, biologics, and devices; Pharmaceutical drug, biologic, and device development services; Providing scientific and medical research information in the field of clinical trials, pharmaceuticals, biologics, and devices via online searchable database; Research and development of new products for others; Consulting services in the field of contract clinical research and contract product development; Consulting services for others in the field of design, planning, and implementation project management of clinical trials; Providing medical and scientific research information to physicians, medical professionals, healthcare professionals, patients, and medical, pharmaceutical, and biotechnology companies; Providing an online non-downloadable database in the field of clinical trials; Online non-downloadable software, software as a service (SaaS), platform as a service (PAAS), and cloud-based software providing clinical trial, pharmaceutical, medical, healthcare, and life sciences information and analysis; Online non-downloadable software, software as a service (SaaS), platform as a service (PAAS), and cloud-based software accessing, collecting, managing, tracking, analyzing, and reporting data in the clinical trial, pharmaceutical, medical, healthcare, and life sciences fields; Online non-downloadable software, software as a service (SaaS), platform as a service (PAAS), and cloud-based software for medical and scientific research and clinical trials; Online non-downloadable software, software as a service (SaaS), platform as a service (PAAS), and cloud-based software for collecting, managing, and analyzing clinical trial data; Online non-downloadable software, software as a service (SaaS), platform as a service (PAAS), and cloud-based software for clinical trial patient matching and analysis; Online non-downloadable software, software as a service (SaaS), platform as a service (PAAS), and cloud-based software; Scientific, medical, and new product development research, consulting, and support services
A classification code parser and method can include: reading a classification code having a description; reading a required keyword, and a total number of keywords associated with the classification code; reading text of a note; tokenizing the text of the note to create a note token stream, the note token stream having a note token and a position of the note token within the note token stream; creating a keyword map including a total number of matched keywords; determining a match ratio from the total number of the matched keywords and the total number of the keywords; determining a proximity factor based on a shortest span of tokens within the note token stream containing all the matched keywords; and determining a strength of a match between the classification code and the note based on the match ratio being multiplied by the proximity factor.
A computer-assisted method to timely provide notifications of treatments, the method including receiving de-identified longitudinal medical records, each de-identified longitudinal medical record representing a record of a different anonymized patient and encoding information identifying a treatment received by the anonymized patient and receiving notification data including notification records, each notification record encoding information identifying a channel through which the notification was provided. The method includes determining a first channel impact model representing an impact of a notification provided through a first channel on a treatment being received, a second channel impact model representing an impact of a notification provided through a second channel on a treatment being received, and determining a multi-channel impact model representing an impact of notifications being provided through both the first channel and the second channel on a treatment being received.
G16H 10/60 - ICT specially adapted for the handling or processing of patient-related medical or healthcare data for patient-specific data, e.g. for electronic patient records
G06N 3/088 - Non-supervised learning, e.g. competitive learning
G16H 50/70 - ICT specially adapted for medical diagnosis, medical simulation or medical data miningICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for mining of medical data, e.g. analysing previous cases of other patients
Methods, systems, and apparatus, including computer programs encoded on computer storage media, for anonymizing unstructured data. In some implementations, a server can receive unstructured data. The server can automatically detect attributes in the unstructured data using a trained machine-learning model and can determine an amount of undetected attributes and detected attributes in the unstructured data. The server can simulate additional attributes for the unstructured data according to the amount of undetected attributes. The server can analyze a risk of disclosure in the unstructured data using the detected attributes and the simulated additional attributes. The server can modify the detected attributes according to the analyzed risk of disclosure and replace the detected attributes with the modified detected attributes in the unstructured data.
A computer-implemented method for providing a user with a performance indicator score includes receiving a first transaction message that includes historical clinical-trial performance data from one or more processors at a clinical research organization and receiving a second transaction message with health records data with parameters indicative of insurance claims data. The received historical clinical-trial performance data and the prescription data is translated into an updated database. Related records within the updated database are identified and one or more key performance indicators included in the data at the updated database for a first physician are identified. A score for each of the one or more key performance indicators are calculated and a performance indicator score record for the first physician is generated based on the calculated scores for each of the one or more key performance indicators. A multi-dimensional chart for organizing and evaluating investigators is generated.
G16H 10/20 - ICT specially adapted for the handling or processing of patient-related medical or healthcare data for electronic clinical trials or questionnaires
G16H 10/60 - ICT specially adapted for the handling or processing of patient-related medical or healthcare data for patient-specific data, e.g. for electronic patient records
G16H 40/20 - ICT specially adapted for the management or administration of healthcare resources or facilitiesICT specially adapted for the management or operation of medical equipment or devices for the management or administration of healthcare resources or facilities, e.g. managing hospital staff or surgery rooms
G16H 40/63 - ICT specially adapted for the management or administration of healthcare resources or facilitiesICT specially adapted for the management or operation of medical equipment or devices for the operation of medical equipment or devices for local operation
23.
Reconciliation of data across distinct feature sets
Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for linking a first electronic data set to a second set of data fields in a second electronic data set. Automatically identifying a prescribing physician identifier based on the linked first and second electronic data sets. Determining a relationship between a physician associated with the prescribing physician identifier and at least one of the approved entities based on comparing the prescribing physician identifier and identifiers of the one or more approved entities to a fourth set of data fields from a fourth electronic data set. Automatically generating an electronic notification indicating that a product sold by the merchant is eligible for the discount in response to determining a relationship between a physician and the at least one of the approved entities.
G16H 20/10 - ICT specially adapted for therapies or health-improving plans, e.g. for handling prescriptions, for steering therapy or for monitoring patient compliance relating to drugs or medications, e.g. for ensuring correct administration to patients
G16H 20/13 - ICT specially adapted for therapies or health-improving plans, e.g. for handling prescriptions, for steering therapy or for monitoring patient compliance relating to drugs or medications, e.g. for ensuring correct administration to patients delivered from dispensers
G16H 40/67 - ICT specially adapted for the management or administration of healthcare resources or facilitiesICT specially adapted for the management or operation of medical equipment or devices for the operation of medical equipment or devices for remote operation
A requirements to test (R2T) system is implemented, which provides an automated system by which a user interface (UI)-test automation script package is generated and the generated test scripts therein are executed against software. A visualized workflow is translated into some machine-consumable formatted file. The translated workflow is utilized by an artificial intelligence driven automated R2T engine to discover paths through the workflow, a series of executable steps for the paths that detail how the software will be used, and ultimately test scripts that are generated using pre-defined validation templates. An automation platform executes the test scripts through the software associated with the workflow, which automatically captures evidence of the executed test scripts to fulfill computer system validation requirements. The R2T system provides an automated solution for test script creation and system validation to expedite the validation process and thereby streamline a software's time to market.
A computer-assisted method to timely provide notifications of treatments, the method including receiving de-identified longitudinal medical records, receiving notification data, identifying anonymized patients that received the treatment, identifying notifications for the treatment that were received by the recipients, determining, for each of the identified notifications, whether the recipient is an anonymized patient identified as having received the treatment, determining, for each of the identified notifications for the treatment determined to be received by a recipient that is an anonymized patient identified as having received the treatment, a time relationship between the time when the treatment was received by the anonymized patient and the time that the notification was received by the recipient that is the anonymized patient, and determining, for each of the anonymized patients that received the treatment, associations between one or more time relationships for notifications received by the anonymized patient.
G16H 40/20 - ICT specially adapted for the management or administration of healthcare resources or facilitiesICT specially adapted for the management or operation of medical equipment or devices for the management or administration of healthcare resources or facilities, e.g. managing hospital staff or surgery rooms
G16H 50/70 - ICT specially adapted for medical diagnosis, medical simulation or medical data miningICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for mining of medical data, e.g. analysing previous cases of other patients
A method comprises training a machine-learning system using one or more mitigation actions to apply to one or more encountered risks and identifying tasks to be completed onsite. The method also includes monitoring site workflow using the trained machine learning system to identify the tasks to be completed and the one or more risks that occur onsite. The method further comprises reporting findings from the site monitoring to a reporting visit site as the site monitoring is being performed.
A method comprises training a machine-learning system using one or more mitigation actions to apply to one or more encountered risks and identifying tasks to be completed onsite. The method also includes monitoring site workflow using the trained machine learning system to identify the tasks to be completed and the one or more risks that occur onsite. The method further comprises reporting findings from the site monitoring to a reporting visit site as the site monitoring is being performed.
A method comprises training an artificial intelligence (AI)/machine-learning (ML) system to identify one or more issues at sites, studies, or customer portfolios. The method also includes applying the trained AI/ML system to identify one or more issues at the sites, studies, or customer portfolios. The method also includes identifying one or more risks from the one or more identified issues at the sites, studies, or customer portfolios by one or more clinical leads. The one or more clinical leads identify a cause for the one or more identified risks among statistical composite risks, investigator risks, monitoring risks, and/or recruitment risks. The method also includes identifying mitigation actions for the one or more identified risks by using insights from past performance. The method also includes applying the mitigation actions onto the one or more identified risks.
A method comprises training an artificial intelligence (Al)/ machine-learning (ML) system to identify one or more issues at sites, studies, or customer portfolios. The method also includes applying the trained Al/ ML system to identify one or more issues at the sites, studies, or customer portfolios. The method also includes identifying one or more risks from the one or more identified issues at the sites, studies, or customer portfolios by one or more clinical leads. The one or more clinical leads identify a cause for the one or more identified risks among statistical composite risks, investigator risks, monitoring risks, and/or recruitment risks. The method also includes identifying mitigation actions for the one or more identified risks by using insights from past performance. The method also includes applying the mitigation actions onto the one or more identified risks.
A method includes receiving data images of patient medications. The method also creates a training set using the received data images. The method also includes training a machine learning system using the training set. The machine learning system is trained to monitor shipment and inventory of the patient medications, patient enrollment in medical trials, and a distribution of the patient medications. The method also includes applying the trained machine learning system with monitoring results of the shipment and inventory of the patient medications, the patient enrollment in the medical trials, and the distribution of the patient medications.
G16H 20/10 - ICT specially adapted for therapies or health-improving plans, e.g. for handling prescriptions, for steering therapy or for monitoring patient compliance relating to drugs or medications, e.g. for ensuring correct administration to patients
G16H 50/20 - ICT specially adapted for medical diagnosis, medical simulation or medical data miningICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for computer-aided diagnosis, e.g. based on medical expert systems
G16H 10/20 - ICT specially adapted for the handling or processing of patient-related medical or healthcare data for electronic clinical trials or questionnaires
A method includes accessing genomic data from a first cohort and a second cohort of patients that are encrypted to comprise a probabilistic and irreversible hash of each patient's genomic sequence data; based on the probabilistic and irreversible hashes, determining one or more variants residing in a particular locale indicating where the one or more variants reside; comparing a first number of variants determined to reside in the particular locale for the first cohort of patients with a second number of variants determined to reside in the particular locale for the second cohort of patients; and in response to determining that the first number of variants determined to reside in the particular locale for the first cohort of patients and the second number of variants determined to reside in the particular locale for the second cohort of patients differ by more than a threshold value, identifying the particular locale.
Methods and systems to train and use an ensemble of artificial intelligence/machine learning (AI/ML) models to extract information from social determinants of health (SDoH), including training each of multiple dimensionality reduction models to reduce dimensionality of socio-demographic variables associated with a respective one of multiple SDoH categories, training a predictive model to predict a patient behavior for a geographic region (e.g., risk of non-adherence to treatment regimens) based on dimensionally reduced SDoH (alone or in combination with selected socio-demographic variables and/or other data), training a patient classification model to classify patients based on prescription transactions, and/or training a regional similarity model to determine a measure of similarity between geographic regions based on SDoH and/or dimensionally reduced SDoH. Also disclosed are techniques to visually represent outputs of the models on a user-interactive display.
Documents in source natural languages are translated into target natural languages using a computer-implemented translation that is configured to operate within the domain of the subject matter of the documents that imposes specialized requirements for translation and readability. Subject matter specific documents typically include domain-specific terminology, are subject to various regulatory guidelines, and have different readability requirements depending on the intended reader. The computer-implemented translation applies machine-learning techniques that deconstruct elements of the subject matter specific document into a standard data structure and perform pre-processing steps to tokenize digitized document text to identify the correct sentence structure and syntax for the target natural language to optimize translation by, e.g., a neural machine translation engine. The text segments that are input into the neural machine translation engine are generated to be semantically meaningful in the target natural language to thereby enhance the understanding of the neural machine translation engine.
G16H 10/20 - ICT specially adapted for the handling or processing of patient-related medical or healthcare data for electronic clinical trials or questionnaires
G06N 3/04 - Architecture, e.g. interconnection topology
09 - Scientific and electric apparatus and instruments
Goods & Services
Downloadable computer software for soliciting, collecting, managing, analyzing, reporting, and visualizing market research, market intelligence, and business information; Downloadable software for use in data collection, data management and data analysis of pharmaceutical, medical, healthcare, and life sciences information; Downloadable computer software for conducting, managing, and analyzing surveys and survey results; Downloadable computer software for conducting business research and marketing surveys; downloadable computer software featuring market research, market intelligence, and business information
Methods, systems, and apparatus, including computer programs encoded on computer storage media, for anonymizing unstructured data. In some implementations, a server can receive unstructured data. The server can automatically detect attributes in the unstructured data using a trained machine-learning model and can determine an amount of undetected attributes and detected attributes in the unstructured data. The server can simulate additional attributes for the unstructured data according to the amount of undetected attributes. The server can analyze a risk of disclosure in the unstructured data, using the detected attributes and the simulated additional attributes. The server can modify the detected attributes according to the analyzed risk of disclosure and replace the detected attributes with the modified detected attributes in the unstructured data.
Methods, systems, and apparatus for identifying an adverse event. In one aspect, a method includes obtaining first patient data; applying a machine learning model to the first patient data to identify information indicative of a first adverse event in the first patient data, in which the machine learning model is configured to: identify one or more named entities present in the first patient data; identify information indicative of the first adverse event based on the identified named entities; and output annotated patient data; obtaining feedback data on the annotated patient data, in which the feedback data is usable to refine the machine learning model; applying the refined machine learning model to second patient data to identify information indicative of a second adverse event in the second patient data; and providing information indicative of the second adverse events identified in the second patient data.
G16H 10/20 - ICT specially adapted for the handling or processing of patient-related medical or healthcare data for electronic clinical trials or questionnaires
G16H 10/60 - ICT specially adapted for the handling or processing of patient-related medical or healthcare data for patient-specific data, e.g. for electronic patient records
G16H 15/00 - ICT specially adapted for medical reports, e.g. generation or transmission thereof
37.
SYSTEM AND METHOD FOR AUTOMATED ADVERSE EVENT IDENTIFICATION
Methods, systems, and apparatus for identifying an adverse event. In one aspect, a method includes obtaining first patient data; applying a machine learning model to the first patient data to identify information indicative of a first adverse event in the first patient data, in which the machine learning model is configured to: identify one or more named entities present in the first patient data; identify information indicative of the first adverse event based on the identified named entities; and output annotated patient data; obtaining feedback data on the annotated patient data, in which the feedback data is usable to refine the machine learning model; applying the refined machine learning model to second patient data to identify information indicative of a second adverse event in the second patient data; and providing information indicative of the second adverse events identified in the second patient data.
G16H 50/20 - ICT specially adapted for medical diagnosis, medical simulation or medical data miningICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for computer-aided diagnosis, e.g. based on medical expert systems
38.
AUTOMATED REGULATORY DECISION-MAKING FOR COMPLIANCE
A computer-implemented method includes receiving, by a machine learning model, a question associated with healthcare compliance from a user; identifying, by the machine learning model, a healthcare compliance regulation document associated with the question and one or more healthcare compliance requirements corresponding to the healthcare compliance regulation document; and recommending, by the machine learning model, a decision satisfying the one or more healthcare compliance requirements to the user.
09 - Scientific and electric apparatus and instruments
Goods & Services
Downloadable software for use in data collection, data management and data analysis of pharmaceutical, medical, healthcare, and life sciences information; Electronic database recorded on computer media containing pharmaceutical, medical, healthcare, and life sciences information; Downloadable software for collecting, managing, and analyzing clinical trial data and information in the pharmaceutical, medical, healthcare and life sciences fields; Downloadable software for collecting, managing, and analyzing data related to patient registries, quality improvement, patient support, and patient management in the pharmaceutical, medical, healthcare, and life sciences fields; Downloadable software using artificial intelligence for machine learning, data mining, data analysis, business intelligence analytics, recommendations, and predictive analytics; Downloadable software using artificial intelligence for machine learning, data mining, data analysis, business intelligence analytics, recommendations, and predictive analytics in the healthcare, pharmaceutical, medical, and life sciences industries; Downloadable software featuring machine learning for data searching, recognition, mining, extraction, indexing, sharing, transmitting, capture, and making recommendations in the healthcare, pharmaceutical, medical, and life sciences industries
42 - Scientific, technological and industrial services, research and design
44 - Medical, veterinary, hygienic and cosmetic services; agriculture, horticulture and forestry services
45 - Legal and security services; personal services for individuals.
Goods & Services
Market analysis and research services; Business marketing consulting; Marketing consulting; Business consulting, business management, and business information services; Business consulting services and business management consulting in the pharmaceutical, medical, healthcare, and life sciences fields; Providing business information in the pharmaceutical, medical, healthcare, and life sciences industries via an online portal; Computerized database management and data processing services in the pharmaceutical, medical, healthcare, and life sciences fields; Business consultation and management regarding marketing activities, launching of new products and services, and sales; Business consulting and management in the field of clinical trials, namely, clinical data and regulatory submission management on behalf of pharmaceutical, medical, healthcare, and life sciences companies to assist them with clinical research, clinical trials and applications for drug, biologic and device approval Software as a service (SAAS) services featuring software for providing pharmaceutical, medical, healthcare, and life sciences information and analysis; platform as a service (PAAS) services featuring computer software platforms for providing pharmaceutical, medical, healthcare, and life sciences information and analysis; cloud computing featuring software for providing pharmaceutical, medical, healthcare, and life sciences information and analysis; providing online non-downloadable software for providing pharmaceutical, medical, healthcare, and life sciences information and analysis; Software as a service (SAAS) services featuring software for accessing, collecting, managing, tracking, analyzing, and reporting data in the pharmaceutical, medical, healthcare, and life sciences fields; platform as a service (PAAS) services featuring computer software platforms for accessing, collecting, managing, tracking, analyzing, and reporting data in the pharmaceutical, medical, healthcare, and life sciences fields; cloud computing featuring software for accessing, collecting, managing, tracking, analyzing, and reporting data in the pharmaceutical, medical, healthcare, and life sciences fields; providing online non-downloadable software used for accessing, collecting, managing, tracking, analyzing, and reporting data in the pharmaceutical, medical, healthcare, and life sciences fields; Software as a service (SAAS) services featuring software using artificial intelligence for machine learning, data mining, data analysis, business intelligence analytics, recommendations, and predictive analytics; platform as a service (PAAS) services featuring computer software platforms using artificial intelligence for machine learning, data mining, data analysis, business intelligence analytics, recommendations, and predictive analytics; cloud computing featuring software using artificial intelligence for machine learning, data mining, data analysis, business intelligence analytics, recommendations, and predictive analytics; providing online non-downloadable software using artificial intelligence for machine learning, data mining, data analysis, business intelligence analytics, recommendations, and predictive analytics; Software as a service (SAAS) services featuring software using artificial intelligence for machine learning, data mining, data analysis, business intelligence analytics, recommendations, and predictive analytics in the in the healthcare, pharmaceutical, medical, and life sciences industries; platform as a service (PAAS) services featuring computer software platforms using artificial intelligence for machine learning, data mining, data analysis, business intelligence analytics, recommendations, and predictive analytics in the in the healthcare, pharmaceutical, medical, and life sciences industries; cloud computing featuring software using artificial intelligence for machine learning, data mining, data analysis, business intelligence analytics, recommendations, and predictive analytics in the in the healthcare, pharmaceutical, medical, and life sciences industries; providing online non-downloadable software using artificial intelligence for machine learning, data mining, data analysis, business intelligence analytics, recommendations, and predictive analytics in the in the healthcare, pharmaceutical, medical, and life sciences industries; Software as a service (SAAS) services featuring software featuring machine learning for data searching, recognition, mining, extraction, indexing, sharing, transmitting, capture, and making recommendations in the healthcare, pharmaceutical, medical, and life sciences industries; platform as a service (PAAS) services featuring computer software platforms featuring machine learning for data searching, recognition, mining, extraction, indexing, sharing, transmitting, capture, and making recommendations in the healthcare, pharmaceutical, medical, and life sciences industries; cloud computing featuring software featuring machine learning for data searching, recognition, mining, extraction, indexing, sharing, transmitting, capture, and making recommendations in the healthcare, pharmaceutical, medical, and life sciences industries; providing online non-downloadable software featuring machine learning for data searching, recognition, mining, extraction, indexing, sharing, transmitting, capture, and making recommendations in the healthcare, pharmaceutical, medical, and life sciences industries; Information technology consulting relating to installation, maintenance, design, development, implementation, repair, use, and application of computer software; Computer software consultation services in the pharmaceutical, medical, healthcare, and life sciences fields; Scientific research in the pharmaceutical, medical, healthcare, and life sciences fields; Computer services, namely, creating computer network-based indexes and databases of information; Providing electronic data capture and data management systems, namely, providing temporary use of online, non-downloadable software for collection and management of healthcare, medical, pharmaceutical and life sciences data; Software as a service (SAAS) services featuring software for collecting, managing, and analyzing clinical trial data; platform as a service (PAAS) services featuring software for collecting, managing, and analyzing clinical trial data; cloud computing featuring software for collecting, managing, and analyzing clinical trial data; providing online non-downloadable software for collecting, managing, and analyzing clinical trial data; Providing an on-line interactive database featuring scientific research information in the pharmaceutical, medical, healthcare, and life sciences fields; Providing information relating to the development, and validation of drugs, biologics and devices, namely, providing information in the field of new product development and product testing Providing health and medical information; Providing health and medical information to others relating to health management and disease management; Providing medical information in the field of pharmaceuticals Providing regulatory information, namely, providing legal information services regarding compliance with pharmaceutical, medical, healthcare, and life sciences regulations; Providing an online, interactive computer database in the field of regulatory compliance consultancy relating to the pharmaceutical, medical, healthcare, and life sciences fields
41.
AUTOMATED REGULATORY DECISION-MAKING FOR COMPLIANCE
A computer-implemented method includes receiving, by a machine learning model, a question associated with healthcare compliance from a user; identifying, by the machine learning model, a healthcare compliance regulation document associated with the question and one or more healthcare compliance requirements corresponding to the healthcare compliance regulation document; and recommending, by the machine learning model, a decision satisfying the one or more healthcare compliance requirements to the user.
G16H 40/20 - ICT specially adapted for the management or administration of healthcare resources or facilitiesICT specially adapted for the management or operation of medical equipment or devices for the management or administration of healthcare resources or facilities, e.g. managing hospital staff or surgery rooms
Methods, systems, and apparatus, including computer programs encoded on computer storage media, for responding to a query. In some implementations, a computer obtains a query. The computer determines a meaning for each term in the query. The computer determines user data for the user that submitted the query. The computer identifies one or more ontologies based on the meanings for at least some of the terms. The computer identifies a knowledge graph based on the identified ontologies and the user data. The computer generates a response to the query by traversing a path of the identified knowledge graph to identify items in the knowledge graph based on the determined meaning for each of the terms. The computer generates path data that represents the path taken by the computer through the identified knowledge graph. The computer provides the generated response and the path data to the client device.
G16H 50/70 - ICT specially adapted for medical diagnosis, medical simulation or medical data miningICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for mining of medical data, e.g. analysing previous cases of other patients
Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for generating a predictive system that obtains and processes data describing terms for different medical concepts to generate commands from a user query. An entity module of the system determines whether a term describes a medical entity associated with a healthcare condition affecting an individual. When the term describes the medical entity an encoding module links the medical entity with a specified category based on an encoding scheme. The system receives the user query. A parsing engine of the system uses the received query to generate a machine-readable command by parsing the query against terms that describe the medical entity and based on the encoding scheme for linking the medical entity to the specified category. The system uses the command to query different databases to obtain data for generating a response to the received query.
G06F 16/383 - Retrieval characterised by using metadata, e.g. metadata not derived from the content or metadata generated manually using metadata automatically derived from the content
Embodiments of the present disclosure provide a method for monitoring/tracking the lifecycle of a drug from build (e.g., as part of clinical trial development), to approval (e.g., regulatory), to in-market (e.g., distribution and safety information). The use of artificial intelligence (AI) and blockchain technology may enable the system to track the drug down to the prescription level and may support a digital label that can be updated as necessary based on such monitoring (e.g., that can be amended based on safety information detected while the drug is in market and warnings sent out upon amendment).
G06F 16/955 - Retrieval from the web using information identifiers, e.g. uniform resource locators [URL]
G16H 20/00 - ICT specially adapted for therapies or health-improving plans, e.g. for handling prescriptions, for steering therapy or for monitoring patient compliance
G06K 7/14 - Methods or arrangements for sensing record carriers by electromagnetic radiation, e.g. optical sensingMethods or arrangements for sensing record carriers by corpuscular radiation using light without selection of wavelength, e.g. sensing reflected white light
G06K 19/06 - Record carriers for use with machines and with at least a part designed to carry digital markings characterised by the kind of the digital marking, e.g. shape, nature, code
45.
DECISION SUPPORT SYSTEM FOR MARKETING MIX MODELING
Methods, systems, and apparatus, including computer programs encoded on computer storage media, for generating models. In some implementations, a system obtains data that comprises promotions and parameters for an opportunity. The system generates transformation spaces based on the promotions and the parameters, wherein each transformation space comprises states, each state is based on the parameters for a particular promotion. The system iterates over a number of iterations. For each transformation space, the system adjusts a state of the transformation space based on actions. The system generates a model by combining each adjusted state. The system generates an entropy for the model. The system compares the entropy to a threshold value, wherein the threshold value corresponds to one of the parameters. In response to determining that the entropy exceeds the threshold value, the system iterates. The system provides the generated model for output.
Documents in source natural languages are translated into target natural languages using a computer-implemented translation that is configured to operate within the domain of the subject matter of the documents that imposes specialized requirements for translation and readability. Subject matter specific documents typically include domain-specific terminology, are subject to various regulatory guidelines, and have different readability requirements depending on the intended reader. The computer-implemented translation applies machine-learning techniques that deconstruct elements of the subject matter specific document into a standard data structure and perform pre-processing steps to tokenize digitized document text to identify the correct sentence structure and syntax for the target natural language to optimize translation by, e.g., a neural machine translation engine. The text segments that are input into the neural machine translation engine are generated to be semantically meaningful in the target natural language to thereby enhance the understanding of the neural machine translation engine.
G16H 10/20 - ICT specially adapted for the handling or processing of patient-related medical or healthcare data for electronic clinical trials or questionnaires
G06N 3/04 - Architecture, e.g. interconnection topology
Some implementations provide a computer-implemented method for identifying, from on-line postings, reports of potential adverse effects resulting from consuming a healthcare product, the method including: receiving a log of on-line postings regarding consuming the healthcare product; receiving a database comprising a healthcare taxonomy and a set of linguistic rules; analyzing, based on the healthcare taxonomy, the log of on-line postings to identify a report of at least one adverse effect resulting from consuming the healthcare product; generating a score for the identified report according to the healthcare taxonomy and the set of linguistic rules; comparing the generated score with a threshold; and in response to determining that the generated score is above the threshold, flagging the identified report as a report of a potential adverse effect.
G16H 20/10 - ICT specially adapted for therapies or health-improving plans, e.g. for handling prescriptions, for steering therapy or for monitoring patient compliance relating to drugs or medications, e.g. for ensuring correct administration to patients
G16H 70/40 - ICT specially adapted for the handling or processing of medical references relating to drugs, e.g. their side effects or intended usage
48.
METHODS AND SYSTEMS TO IDENTIFY COLLABORATIVE COMMUNITIES FROM MULTIPLEX HEALTHCARE PROVIDERS
Methods and systems to identify collaborative communities of individuals from graphs of multiple types of relationships amongst the individuals, including to mine data related to multiple types of relationships amongst individuals, construct graphs to represent the respective types of relationships amongst individuals, and perform a multiplex graph convolutional network (MGCN) artificial intelligence machine learning (AIML) analysis across the multiple graphs to identify the collaborative communities. A mathematical representation of the graphs may be learned/tuned to optimize clustering of the individuals. Multiple parameters (inter-graph weights, consensus regularization function) may be jointly tuned based on a joint optimization function. The collaborative communities may be displayed such that relative positions of the individuals represent measures of influence exerted by the respective individuals within the respective collaborative communities.
G16H 40/20 - ICT specially adapted for the management or administration of healthcare resources or facilitiesICT specially adapted for the management or operation of medical equipment or devices for the management or administration of healthcare resources or facilities, e.g. managing hospital staff or surgery rooms
A classification code parser and method can include: reading a classification code having a description; reading a required keyword, and a total number of keywords associated with the classification code; reading text of a note; tokenizing the text of the note to create a note token stream, the note token stream having a note token and a position of the note token within the note token stream; creating a keyword map including a total number of matched keywords; determining a match ratio from the total number of the matched keywords and the total number of the keywords; determining a proximity factor based on a shortest span of tokens within the note token stream containing all the matched keywords; and determining a strength of a match between the classification code and the note based on the match ratio being multiplied by the proximity factor.
Cohort definition and selection system for a computer having a memory, a central processing unit and a display, the system including: a cohort definition module to configure the memory according to a phenotype vector. The phenotype vector includes a patient ID to uniquely associate the phenotype vector to a patient, a plurality of demographic dimension fields, each demographic dimension field to describe a respective demographic aspect of the patient, a calculated dimension field to describe a calculated information related to the patient, a plurality of phenotype-based dimension fields, each phenotype-based dimension field to indicate relevance of the respective phenotype-based dimension field to the patient, and a child phenotype vector to recursively define a phenotype-based dimension field, and a cohort selection module to select a set of phenotype vectors that are within a predetermined error from a cohort selection criteria.
G16H 10/20 - ICT specially adapted for the handling or processing of patient-related medical or healthcare data for electronic clinical trials or questionnaires
51.
AUTOMATED CLASSIFICATION AND INTERPRETATION OF LIFE SCIENCE DOCUMENTS
A computer-implemented tool for automated classification and interpretation of documents, such as life science documents supporting clinical trials, is configured to perform a combination of raw text, document construct, and image analyses to enhance classification accuracy by enabling a more comprehensive machine-based understanding of document content. The combination of analyses provides context for classification by leveraging relative spatial relationships among text and image elements, identifying characteristics and formatting of elements, and extracting additional metadata from the documents as compared to conventional automated classification tools, wherein natural language processing (NLP) is applied to associate text with tokens, and relevant differences and similarities between protocols are identified.
A method includes patient data from a centralized database to identify protocol deviations from the patient data. Natural language processing or machine-learning is performed by a cloud computing server to perform content extraction on the protocol deviations, wherein the content extraction is performed to extract keywords, phrases, and supervised text, wherein the extracted keywords, phrases, and supervised text are used to group the protocol deviations by content. The method also includes reporting, to a user interface, multiple statistical summaries of the protocol deviations, wherein the multiple statistical summaries include a patient, site, study, and country.
G16H 50/70 - ICT specially adapted for medical diagnosis, medical simulation or medical data miningICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for mining of medical data, e.g. analysing previous cases of other patients
G16H 10/20 - ICT specially adapted for the handling or processing of patient-related medical or healthcare data for electronic clinical trials or questionnaires
G16H 10/60 - ICT specially adapted for the handling or processing of patient-related medical or healthcare data for patient-specific data, e.g. for electronic patient records
G16H 40/20 - ICT specially adapted for the management or administration of healthcare resources or facilitiesICT specially adapted for the management or operation of medical equipment or devices for the management or administration of healthcare resources or facilities, e.g. managing hospital staff or surgery rooms
A parser is deployed early in a machine learning pipeline to read raw data and collect useful statistics about the raw data's content to determine which items of raw data exhibit a proxy for feature importance for the machine learning model. The parser operates at high speeds that approach the disk's absolute throughput while utilizing a small memory footprint. Utilization of the parser enables the machine learning pipeline to receive a fraction of the total raw data that would otherwise be available. Several scans through the data are performed, by which proxies for feature importance are indicated and irrelevant features may be discarded and thereby not forwarded to the machine learning pipeline. This reduces the amount of memory and other hardware resources used at the server and also expedites the machine learning process.
A computer-implemented includes a computing system receiving one or more queries. The computing system includes one or more compute nodes that perform computations for determining a response to at least one query. The system stores, in a storage device, domain data that includes at least one of: a dataset, a metric associated with the domain data, a query time, or a usage pattern that is based, in part, on the one or more queries. The method includes the system generating a distribution model based on analysis of the domain data. The distribution model is generated using machine learning logic executed by the system. The method further includes the system using the distribution model to distribute data to the one or more compute nodes. The distributed data is used to determine, within a threshold response time, the response to the at least one query.
Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for obtaining data for a set of patients that each have a certain condition. A first and second sequence of data is determined based on the obtained data. A scoring model is generated by processing the first and second sequence of data to train a neural network. The scoring model determines a confidence that an individual has the particular healthcare condition. Patient scoring data is provided to the scoring model to determine the confidence that the individual has the healthcare condition. A confidence score is received as an output of the scoring model in response to providing the patient scoring data. The confidence score represents a determined confidence that the individual has the healthcare condition. An indication that represents the confidence that the individual has the healthcare condition is provided based on the received confidence score.
G16H 50/30 - ICT specially adapted for medical diagnosis, medical simulation or medical data miningICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for calculating health indicesICT specially adapted for medical diagnosis, medical simulation or medical data miningICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for individual health risk assessment
G16H 10/60 - ICT specially adapted for the handling or processing of patient-related medical or healthcare data for patient-specific data, e.g. for electronic patient records
G06N 3/04 - Architecture, e.g. interconnection topology
56.
System and method for longitudinal non-conforming medical data records
A computer-assisted method including obtaining healthcare records from multiple different data sources that each provide information regarding a corresponding type of healthcare events, identifying healthcare records from the multiple different data sources that are for a healthcare event associated with a particular individual and that occurred during a particular period of time, and generating a composite record for the particular individual for the particular period of time, and storing the composite record in a database of composite records. The composite record include an identifier for the particular individual, a pharmaceutical transactions array, where each entry in the pharmaceutical transactions array represents a pharmaceutical transaction that occurred during the particular period of time, and a medical visit array, where each entry in the medical visit array represents a medical visit that occurred during the particular period of time.
G16H 10/60 - ICT specially adapted for the handling or processing of patient-related medical or healthcare data for patient-specific data, e.g. for electronic patient records
G16H 20/10 - ICT specially adapted for therapies or health-improving plans, e.g. for handling prescriptions, for steering therapy or for monitoring patient compliance relating to drugs or medications, e.g. for ensuring correct administration to patients
H04L 9/30 - Public key, i.e. encryption algorithm being computationally infeasible to invert and users' encryption keys not requiring secrecy
57.
System and method to regularize cancer treatment data for systematic recording
Implementations provide a method to consolidate data records of regimens for treating oncology conditions. The method includes: accessing data records each encoding multi-tier data characteristics of a regimen for treating a particular oncology condition; receiving a first data record encoding a first regimen specific to a first healthcare provider institution; parsing the first data record according to a hierarchy of the encoded multi-tier data characteristics; distributing a respective weight to each of the encoded data characteristics to account for the potentially missing data characteristic; comparing data characteristics of the first data record with data characteristics from the data records by applying the respective weight to each data characteristic at a particular tier of the hierarchy such that a respective compound score is generated for each data record; and based on the compound scores for all data records, determining a prevailing data record of regimen as matching the first data record.
G06F 16/25 - Integrating or interfacing systems involving database management systems
G16H 20/10 - ICT specially adapted for therapies or health-improving plans, e.g. for handling prescriptions, for steering therapy or for monitoring patient compliance relating to drugs or medications, e.g. for ensuring correct administration to patients
G16H 70/20 - ICT specially adapted for the handling or processing of medical references relating to practices or guidelines
G16H 50/70 - ICT specially adapted for medical diagnosis, medical simulation or medical data miningICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for mining of medical data, e.g. analysing previous cases of other patients
Methods, systems, and apparatus, including computer programs encoded on computer storage media, for responding to a query. In some implementations, a computer obtains a query. The computer determines a meaning for each term in the query. The computer determines user data for the user that submitted the query. The computer identifies one or more ontologies based on the meanings for at least some of the terms. The computer identifies a knowledge graph based on the identified ontologies and the user data. The computer generates a response to the query by traversing a path of the identified knowledge graph to identify items in the knowledge graph based on the determined meaning for each of the terms. The computer generates path data that represents the path taken by the computer through the identified knowledge graph. The computer provides the generated response and the path data to the client device.
G16H 50/70 - ICT specially adapted for medical diagnosis, medical simulation or medical data miningICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for mining of medical data, e.g. analysing previous cases of other patients
Methods, systems, and apparatus, including computer programs encoded on computer storage media, for responding to a query. In some implementations, a computer obtains a query. The computer determines a meaning for each term in the query. The computer determines user data for the user that submitted the query. The computer identifies one or more ontologies based on the meanings for at least some of the terms. The computer identifies a knowledge graph based on the identified ontologies and the user data. The computer generates a response to the query by traversing a path of the identified knowledge graph to identify items in the knowledge graph based on the determined meaning for each of the terms. The computer generates path data that represents the path taken by the computer through the identified knowledge graph. The computer provides the generated response and the path data to the client device
G16H 10/20 - ICT specially adapted for the handling or processing of patient-related medical or healthcare data for electronic clinical trials or questionnaires
G16H 15/00 - ICT specially adapted for medical reports, e.g. generation or transmission thereof
G16H 10/60 - ICT specially adapted for the handling or processing of patient-related medical or healthcare data for patient-specific data, e.g. for electronic patient records
G06F 16/00 - Information retrievalDatabase structures thereforFile system structures therefor
Aspects of the subject matter described in this specification are embodied in systems and methods that utilize machine-learning techniques to evaluate clinical trial data using one or more learning models trained to identify anomalies representing adverse events associated with a clinical trial investigation. In some implementations, investigation data collected at a clinical trial site is obtained. A set of models corresponding to the clinical trial site is selected. Each model included in the set of models is trained to identify, based on historical investigation data collected at the clinical trial site, a distinct set of one or more indicators that indicate a compliance risk associated with the investigation data. A score for the clinical trial site is determined based on the investigation data relative to the historical investigation data. The score represents a likelihood that the investigation data is associated with at least one indicator representing the compliance risk.
G16H 10/60 - ICT specially adapted for the handling or processing of patient-related medical or healthcare data for patient-specific data, e.g. for electronic patient records
G16H 50/20 - ICT specially adapted for medical diagnosis, medical simulation or medical data miningICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for computer-aided diagnosis, e.g. based on medical expert systems
G16H 10/20 - ICT specially adapted for the handling or processing of patient-related medical or healthcare data for electronic clinical trials or questionnaires
G16H 20/10 - ICT specially adapted for therapies or health-improving plans, e.g. for handling prescriptions, for steering therapy or for monitoring patient compliance relating to drugs or medications, e.g. for ensuring correct administration to patients
61.
AI AND ML ASSISTED SYSTEM FOR DETERMINING SITE COMPLIANCE USING SITE VISIT REPORT
Methods and systems to automatically construct a clinical study site visit report (SVR), conduct the SVR, evaluate the SVR in real-time, and provide feedback while the SVR is being conducted. Responses to the SVR include user-selectable answers and natural language notes. Each response is evaluated as it is submitted based on a combination of pre-configured rules and a computer-trained model. If an anomaly is detected and is not already captured in the SVR, an alert is generated during performance of the SVR. The alert may include recommended remedial action.
G16H 10/20 - ICT specially adapted for the handling or processing of patient-related medical or healthcare data for electronic clinical trials or questionnaires
Methods and systems to automatically construct a clinical study site visit report (SVR), conduct the SVR, evaluate the SVR in real-time, and provide feedback while the SVR is being conducted. Responses to the SVR include user-selectable answers and natural language notes. Each response is evaluated as it is submitted based on a combination of pre-configured rules and a computer-trained model. If an anomaly is detected and is not already captured in the SVR, an alert is generated during performance of the SVR. The alert may include recommended remedial action.
G16H 10/20 - ICT specially adapted for the handling or processing of patient-related medical or healthcare data for electronic clinical trials or questionnaires
G16H 15/00 - ICT specially adapted for medical reports, e.g. generation or transmission thereof
G16H 40/20 - ICT specially adapted for the management or administration of healthcare resources or facilitiesICT specially adapted for the management or operation of medical equipment or devices for the management or administration of healthcare resources or facilities, e.g. managing hospital staff or surgery rooms
G16H 40/63 - ICT specially adapted for the management or administration of healthcare resources or facilitiesICT specially adapted for the management or operation of medical equipment or devices for the operation of medical equipment or devices for local operation
A GxP (good practice) platform is implemented to enable artificial intelligence (AI) algorithms to be tracked from creation through training and into production. Deployed algorithms are assigned a GxP chain ID that enables identification of production details associated with respective algorithms. Trained algorithms, each of which are respectively associated with a GxP chain ID, are containerized and can be utilized through an application programing interface (API) to provide a service. The GxP chain ID is linked to production details stored within a database, in which the production details can include information such as data used to train the algorithm, a history version, a date/time stamp when the algorithm was validated, software and hardware on which the algorithm was developed and trained, among other details. Changes to the algorithm can be tracked using an immutable ledger facilitated by the implementation of blockchain.
G06F 30/27 - Design optimisation, verification or simulation using machine learning, e.g. artificial intelligence, neural networks, support vector machines [SVM] or training a model
Embodiments of the present disclosure provide a method for monitoring/tracking the lifecycle of a drug from build (e.g., as part of clinical trial development), to approval (e.g., regulatory), to in-market (e.g., distribution and safety information). The use of artificial intelligence (AI) and blockchain technology may enable the system to track the drug down to the prescription level and may support a digital label that can be updated as necessary based on such monitoring (e.g., that can be amended based on safety information detected while the drug is in market and warnings sent out upon amendment).
G06F 16/955 - Retrieval from the web using information identifiers, e.g. uniform resource locators [URL]
G16H 20/00 - ICT specially adapted for therapies or health-improving plans, e.g. for handling prescriptions, for steering therapy or for monitoring patient compliance
G06K 7/14 - Methods or arrangements for sensing record carriers by electromagnetic radiation, e.g. optical sensingMethods or arrangements for sensing record carriers by corpuscular radiation using light without selection of wavelength, e.g. sensing reflected white light
G06K 19/06 - Record carriers for use with machines and with at least a part designed to carry digital markings characterised by the kind of the digital marking, e.g. shape, nature, code
Embodiments of the present disclosure provide a method for monitoring/tracking the lifecycle of a drug from build (e.g., as part of clinical trial development), to approval (e.g., regulatory), to in-market (e.g., distribution and safety information). The use of artificial intelligence (AI) and blockchain technology may enable the system to track the drug down to the prescription level and may support a digital label that can be updated as necessary based on such monitoring (e.g., that can be amended based on safety information detected while the drug is in market and warnings sent out upon amendment).
Systems, methods, devices, and non-transitory, computer-readable storage media are disclosed for matching a service requester with a service provider via a taxonomy based directed graph. The method includes: receiving a keyword associated with a service; accessing a directed graph including a root node and nodes connected by edges, each node having a title; identifying a second node of the directed graph for each of service providers, each second node having a title matching a skill of a respective service provider; determining a distance between the first node and each second node along the directed graph; and ranking the service providers based at least in part on the distance between the first node and each second node. Systems, methods, devices, and non-transitory, computer-readable storage media are further disclosed for determining and storing a quality score for the revised linguistic content.
A method is described that includes receiving a request to translate source content from a first language to a second language. The method includes processing the source content to generate first anonymized content by automatically anonymizing confidential information in the source content. The method also includes providing the first anonymized content to a first service provider to provide anonymization input and processing the first anonymized content with the anonymization input to generate second anonymized content. The method further includes obtaining a machine translation of the second anonymized content from the first language to the second language and providing the machine translation to a second service provider to provide translation input. The method further includes processing the machine translation with the translation input to generate translated content.
A method is described that includes receiving a request to translate source content from a first language to a second language. The method includes processing the source content to generate first anonymized content by automatically anonymizing confidential information in the source content. The method also includes providing the first anonymized content to a first service provider to provide anonymization input and processing the first anonymized content with the anonymization input to generate second anonymized content. The method further includes obtaining a machine translation of the second anonymized content from the first language to the second language and providing the machine translation to a second service provider to provide translation input. The method further includes processing the machine translation with the translation input to generate translated content.
Systems, methods, devices, and non-transitory, computer-readable storage media are disclosed for matching a service requester with a service provider via a taxonomy based directed graph. The method includes: receiving a keyword associated with a service; accessing a directed graph including a root node and nodes connected by edges, each node having a title; identifying a second node of the directed graph for each of service providers, each second node having a title matching a skill of a respective service provider; determining a distance between the first node and each second node along the directed graph; and ranking the service providers based at least in part on the distance between the first node and each second node. Systems, methods, devices, and non-transitory, computer-readable storage media are further disclosed for determining and storing a quality score for the revised linguistic content.
Some implementations may provide a computer-assisted method for master data management, the method including: receiving configuration information defining a model of entities, each entity encoding attributes of a prescriber of one or more healthcare products; receiving specification information defining mapping logic, searching logic, and matching logic, and merging logic for processing base entities and related entities of the model; receiving data from more than one source customer databases, the customer database including data encoding prescribers of healthcare products and being maintained by more than one organizations; translating the received data into staging data according to the mapping logic in the received specification information; generating master data by processing the staging data according to the searching logic, matching logic, and merging logic in the received specification information; and synchronizing at least a portion of the master data to at least one of the source customer databases.
G06Q 10/06 - Resources, workflows, human or project managementEnterprise or organisation planningEnterprise or organisation modelling
G16Z 99/00 - Subject matter not provided for in other main groups of this subclass
G16H 10/60 - ICT specially adapted for the handling or processing of patient-related medical or healthcare data for patient-specific data, e.g. for electronic patient records
G06F 16/25 - Integrating or interfacing systems involving database management systems
G16H 10/20 - ICT specially adapted for the handling or processing of patient-related medical or healthcare data for electronic clinical trials or questionnaires
G06F 16/27 - Replication, distribution or synchronisation of data between databases or within a distributed database systemDistributed database system architectures therefor
71.
Systems and methods for streaming normalized clinical trial capacity information
The invention generally relates to computer-based systems to evaluate and market clinical trial research centers. In certain aspects, the invention provides computer-based systems to collect information about clinical research centers. Systems include a tangible, non-transitory memory coupled to a processor operable to retrieve, based on a user's input, an identity of a clinical research center and prompt the user for information relating generally to the center. The system can collect disease-specific information by prompting the user for a selection of a disease and then collecting from the user information identifying an ability of the center to perform one or more tests relating to the disease.
G16H 10/20 - ICT specially adapted for the handling or processing of patient-related medical or healthcare data for electronic clinical trials or questionnaires
G16H 70/40 - ICT specially adapted for the handling or processing of medical references relating to drugs, e.g. their side effects or intended usage
Market research, market intelligence, and business information services; Computerized market research, market intelligence, and business information services; Conducting online and mobile business research and marketing surveys; Providing a web-based online portal that provides customer access to market research, market intelligence, and business information
73.
Automated classification and interpretation of life science documents
A computer-implemented tool for automated classification and interpretation of documents, such as life science documents supporting clinical trials, is configured to perform a combination of raw text, document construct, and image analyses to enhance classification accuracy by enabling a more comprehensive machine-based understanding of document content. The combination of analyses provides context for classification by leveraging relative spatial relationships among text and image elements, identifying characteristics and formatting of elements, and extracting additional metadata from the documents as compared to conventional automated classification tools.
A skipping natural language parser can include: identifying a candidate location within a string of characters with a processor, the candidate location being an unbroken string of relevant characters followed by an irrelevant character; attempting to parse an attribute from the candidate location with the processor; storing the attribute in a memory based on the attribute being parsed; skipping to a next candidate location based on the attribute being parsed with the processor; and skipping, the relevant characters of the candidate location and the irrelevant character following the candidate location, to the next candidate location based on the attribute not being parsed with the processor.
G10L 21/00 - Speech or voice signal processing techniques to produce another audible or non-audible signal, e.g. visual or tactile, in order to modify its quality or its intelligibility
A skipping natural language parser can include: identifying a candidate location within a string of characters with a processor, the candidate location being an unbroken string of relevant characters followed by an irrelevant character; attempting to parse an attribute from the candidate location with the processor; storing the attribute in a memory based on the attribute being parsed; skipping to a next candidate location based on the attribute being parsed with the processor; and skipping, the relevant characters of the candidate location and the irrelevant character following the candidate location, to the next candidate location based on the attribute not being parsed with the processor.
G10L 21/00 - Speech or voice signal processing techniques to produce another audible or non-audible signal, e.g. visual or tactile, in order to modify its quality or its intelligibility
76.
Professional network-based identification of influential thought leaders and measurement of their influence via deep learning
Embodiments of the present disclosure provide a method for identifying those entities within a network that have the most influence on other entities within the network. A multi-relational network comprising links among a plurality of physicians is generated based on peer network data, wherein each link indicates a first physician that influences a second physician, and a weight of the influence. A decision by a treating physician of the plurality of physicians is decomposed, using a deep learning engine, into a magnitude of peer influence and a magnitude of control factor influence based on the multi-relational network and a plurality of control factors respectively. The magnitude of peer influence among one or more physicians in the multi-relational network is distributed among physicians in the multi-relational network based on the links each physician maintains with other physicians.
G16H 40/20 - ICT specially adapted for the management or administration of healthcare resources or facilitiesICT specially adapted for the management or operation of medical equipment or devices for the management or administration of healthcare resources or facilities, e.g. managing hospital staff or surgery rooms
A skipping natural language parser can include: identifying a candidate location within a string of characters with a processor, the candidate location being an unbroken string of relevant characters followed by an irrelevant character; attempting to parse an attribute from the candidate location with the processor; storing the attribute in a memory based on the attribute being parsed; skipping to a next candidate location based on the attribute being parsed with the processor; and skipping, the relevant characters of the candidate location and the irrelevant character following the candidate location, to the next candidate location based on the attribute not being parsed with the processor.
A GxP (good practice) platform is implemented to enable artificial intelligence (AI) algorithms to be tracked from creation through training and into production. Deployed algorithms are assigned a GxP chain ID that enables identification of production details associated with respective algorithms. Trained algorithms, each of which are respectively associated with a GxP chain ID, are containerized and can be utilized through an application programing interface (API) to provide a service. The GxP chain ID is linked to production details stored within a database, in which the production details can include information such as data used to train the algorithm, a history version, a date/time stamp when the algorithm was validated, software and hardware on which the algorithm was developed and trained, among other details. Changes to the algorithm can be tracked using an immutable ledger facilitated by the implementation of blockchain.
G06F 30/27 - Design optimisation, verification or simulation using machine learning, e.g. artificial intelligence, neural networks, support vector machines [SVM] or training a model
The disclosure generally describes computer-implemented methods, software, and systems for accessing volumes of data records structured to include sets dimensions, each dimension labelled in a manner specific to respective entities; identifying candidates data records keyed by managed keys that span a subset of dimensions even though at least one dimension from the subset of dimensions is labelled differently between the different volumes; comparing the candidate data records from the different volumes to determine whether a particular managed key is valid based on contents of the candidate data records from the different volumes; in response to determining that the particular managed key is valid, combining the candidate data records keyed by the valid managed key to be merged and accessible as one continuous entry; and in response to determining that the particular managed key is invalid, combining the candidate data records from the different volumes as separate entries.
G06F 16/27 - Replication, distribution or synchronisation of data between databases or within a distributed database systemDistributed database system architectures therefor
G06F 16/11 - File system administration, e.g. details of archiving or snapshots
H04L 67/1097 - Protocols in which an application is distributed across nodes in the network for distributed storage of data in networks, e.g. transport arrangements for network file system [NFS], storage area networks [SAN] or network attached storage [NAS]
The invention generally relates to computer-based systems to evaluate and market clinical trial research centers. In certain aspects, the invention provides computer-based systems to collect information about clinical research centers. Systems include a tangible, non-transitory memory coupled to a processor operable to retrieve, based on a user's input, an identity of a clinical research center and prompt the user for information relating generally to the center. The system can collect disease-specific information by prompting the user for a selection of a disease and then collecting from the user information identifying an ability of the center to perform one or more tests relating to the disease.
G16H 10/00 - ICT specially adapted for the handling or processing of patient-related medical or healthcare data
G06F 19/00 - Digital computing or data processing equipment or methods, specially adapted for specific applications (specially adapted for specific functions G06F 17/00;data processing systems or methods specially adapted for administrative, commercial, financial, managerial, supervisory or forecasting purposes G06Q;healthcare informatics G16H)
81.
Management and tracking solution for specific patient consent attributes and permissions
A method of managing consent using a computing device, the consent is given by a subject to one or more events in one or more studies, wherein the consent and the plurality of activities are changeable, the method including: authoring one or more first data forms describing the one or more events and one or more selections responsive to the one or more events; authoring, for each of the plurality of subjects, one or more second data forms including description of a plurality of preferences; forming, for a first of the plurality of subjects, an Informed Consent Forms document by combining the one or more first data forms of a first of the one or more studies and one or more second data forms for the first subject; and generating a manifest indicating the one or more events in the first study to which the first subject has granted consent.
G16H 10/20 - ICT specially adapted for the handling or processing of patient-related medical or healthcare data for electronic clinical trials or questionnaires
G06F 3/0482 - Interaction with lists of selectable items, e.g. menus
82.
Synthesizing complex population selection criteria
System and method to determine a reduced cohort criteria, the method including: defining N selection criteria to select a cohort from among a universe of patient data; querying a patient database, by use of a processor, and by use of the N selection criteria, in order to define a full patient population; selecting a subset of size M of the N selection criteria, to produce a subset criteria; selecting a permutation of the subset criteria, to produce a permuted subset criteria in a predetermined order; for each member of the permuted subset criteria: querying the patient database by use of the member of the permuted subset criteria to produce a respective interim patient population; combining all respective interim patient populations to produce a partial patient population; and calculating, by a processor, a coverage figure of merit that compares the partial patient population to the full patient population.
G16H 10/20 - ICT specially adapted for the handling or processing of patient-related medical or healthcare data for electronic clinical trials or questionnaires
G16Z 99/00 - Subject matter not provided for in other main groups of this subclass
A classification code parser and method can include: reading a classification code having a description; reading a required keyword, and a total number of keywords associated with the classification code; reading text of a note; tokenizing the text of the note to create a note token stream, the note token stream having a note token and a position of the note token within the note token stream; creating a keyword map including a total number of matched keywords; determining a match ratio from the total number of the matched keywords and the total number of the keywords; determining a proximity factor based on a shortest span of tokens within the note token stream containing all the matched keywords; and determining a strength of a match between the classification code and the note based on the match ratio being multiplied by the proximity factor.
G06F 40/274 - Converting codes to wordsGuess-ahead of partial word inputs
G06F 40/284 - Lexical analysis, e.g. tokenisation or collocates
G16H 10/60 - ICT specially adapted for the handling or processing of patient-related medical or healthcare data for patient-specific data, e.g. for electronic patient records
G16H 15/00 - ICT specially adapted for medical reports, e.g. generation or transmission thereof
Systems and techniques are disclosed for using machine-learning to identify potential opportunity patients that are more likely to adjust his/her preference for a healthcare provider or service. In some implementations, integrated patient data is obtained. A patient sequence feature vector, a provider sequence feature vector, and a set of entity-specific feature vectors are generated. A set of opportunity patients is identified. A notification is transmitted to the set of opportunity patients about a second treatment plan.
G16H 50/70 - ICT specially adapted for medical diagnosis, medical simulation or medical data miningICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for mining of medical data, e.g. analysing previous cases of other patients
G16H 10/60 - ICT specially adapted for the handling or processing of patient-related medical or healthcare data for patient-specific data, e.g. for electronic patient records
G16H 40/20 - ICT specially adapted for the management or administration of healthcare resources or facilitiesICT specially adapted for the management or operation of medical equipment or devices for the management or administration of healthcare resources or facilities, e.g. managing hospital staff or surgery rooms
G16H 50/20 - ICT specially adapted for medical diagnosis, medical simulation or medical data miningICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for computer-aided diagnosis, e.g. based on medical expert systems
G16H 20/10 - ICT specially adapted for therapies or health-improving plans, e.g. for handling prescriptions, for steering therapy or for monitoring patient compliance relating to drugs or medications, e.g. for ensuring correct administration to patients
A61B 5/00 - Measuring for diagnostic purposes Identification of persons
Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for generating a predictive system that obtains and processes data describing terms for different medical concepts to generate commands from a user query. An entity module of the system determines whether a term describes a medical entity associated with a healthcare condition affecting an individual. When the term describes the medical entity an encoding module links the medical entity with a specified category based on an encoding scheme. The system receives the user query. A parsing engine of the system uses the received query to generate a machine-readable command by parsing the query against terms that describe the medical entity and based on the encoding scheme for linking the medical entity to the specified category. The system uses the command to query different databases to obtain data for generating a response to the received query.
G06F 16/383 - Retrieval characterised by using metadata, e.g. metadata not derived from the content or metadata generated manually using metadata automatically derived from the content
A computer-implemented method includes a machine learning system receiving distinct types of data associated with multiple individual entities. For each of the individual entities, the machine learning system determines a first attribute that indicates a predicted attribute of the entity based on analysis of the data. The machine learning system also determines a second attribute that indicates a predicted quality attribute of the entity, based on analysis of the data. An attribute weighting module of the machine learning system generates weight values for each of the first attribute and the second attribute of the entity. The machine learning system generates a data structure that identifies a set of entities from among the multiple individual entities, where entities of the set are ranked based on a tier indicator that corresponds to either the first attribute, the second attribute, or both.
Methods, systems, and apparatus, including computer programs encoded on computer storage media, for creating source-specific, persistent patient identifiers for healthcare service providers. One method includes accessing a record of healthcare data, wherein the record includes patient identifying information (PII) associated with one or more persons to whom the healthcare data pertains. The portions of PII included in the accessed record of healthcare data are extracted from the accessed record and encrypted. Based on one or more business rules, one or more hashed tokens are created by applying one or more hashing functions to the extracted portions of PII. A source-specific identifier is received, the source-specific identifier having been encoded in a manner specific to an organization associated with the computer system and having been encoded with reference to the one or more hashed tokens. An association is stored between the source-specific identifier and the accessed record of healthcare data.
G16H 10/60 - ICT specially adapted for the handling or processing of patient-related medical or healthcare data for patient-specific data, e.g. for electronic patient records
G06F 21/62 - Protecting access to data via a platform, e.g. using keys or access control rules
A requirements to test (R2T) system is implemented, which provides an automated system by which a user interface (UI)-test automation script package is generated and the generated test scripts therein are executed against software. A visualized workflow is translated into some machine-consumable formatted file. The translated workflow is utilized by an artificial intelligence driven automated R2T engine to discover paths through the workflow, a series of executable steps for the paths that detail how the software will be used, and ultimately test scripts that are generated using pre-defined validation templates. An automation platform executes the test scripts through the software associated with the workflow, which automatically captures evidence of the executed test scripts to fulfill computer system validation requirements. The R2T system provides an automated solution for test script creation and system validation to expedite the validation process and thereby streamline a software's time to market.
Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for a computing platform that identifies information about a trial program, where the information is related to healthcare data included in datasets, and identifies an investigator based on the information about the trial program. A data analytics model of the platform generates an initial provider score for each provider in a group of providers based on analysis of the information. The analyzed information of the datasets includes healthcare data describing interactions between patients and providers in the group and criteria for the trial program. The platform provides a request to a subset of providers using the initial provider scores. The request is an invitation to for each provider to join a referral network. The platform uses the request to establish referral connections between the trial investigator and a provider in the subset.
G16H 10/20 - ICT specially adapted for the handling or processing of patient-related medical or healthcare data for electronic clinical trials or questionnaires
G16H 50/70 - ICT specially adapted for medical diagnosis, medical simulation or medical data miningICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for mining of medical data, e.g. analysing previous cases of other patients
G16H 80/00 - ICT specially adapted for facilitating communication between medical practitioners or patients, e.g. for collaborative diagnosis, therapy or health monitoring
G16H 50/20 - ICT specially adapted for medical diagnosis, medical simulation or medical data miningICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for computer-aided diagnosis, e.g. based on medical expert systems
Some implementations provide a computer-implemented method that includes retrieving, from a customer relationship (CRM) database, data documenting exposures of healthcare professionals to information of healthcare products from more than one channels and at various time points; processing the retrieved data to model the exposure of each healthcare professional such that an effectiveness of each of the more than one channels for the particular healthcare professional is determined; retrieving, from a prescription database, data recording each healthcare professional prescribing healthcare products at various time points; longitudinally associating the processed data from the customer relationship database and the retrieved data from the prescription database such that a multi-channel CRM and prescription database is generated; and, determining a next healthcare professional to whom information of a particular healthcare product should be directed as well as a channel for the next healthcare professional to receive the information of the particular healthcare product.
G06F 19/00 - Digital computing or data processing equipment or methods, specially adapted for specific applications (specially adapted for specific functions G06F 17/00;data processing systems or methods specially adapted for administrative, commercial, financial, managerial, supervisory or forecasting purposes G06Q;healthcare informatics G16H)
A computer-implemented method for performing quality review of life science documents is described. One or more of the life science documents are scanned by a mobile device, wherein the one or more life science documents are sent to a database. Language, image, rotation, and noise are among the content that is checked among the life science documents, and wherein similarities, suspicious changes, document layouts, and missing sections are checked among the one or more life science documents. In addition, feedback is sent by a system to an originator of the life science documents based on the content regarding imaging, rotation, and noise and the similarities, suspicious changes, document layouts and missing sections.
A requirements to test (R2T) system is implemented, which provides an automated system by which a user interface (UI)-test automation script package is generated and the generated test scripts therein are executed against software. A visualized workflow is translated into some machine-consumable formatted file. The translated workflow is utilized by an artificial intelligence driven automated R2T engine to discover paths through the workflow, a series of executable steps for the paths that detail how the software will be used, and ultimately test scripts that are generated using pre-defined validation templates. An automation platform executes the test scripts through the software associated with the workflow, which automatically captures evidence of the executed test scripts to fulfill computer system validation requirements. The R2T system provides an automated solution for test script creation and system validation to expedite the validation process and thereby streamline a software's time to market.
Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for a computing system that identifies information about a trial program. The information is related to healthcare data for a subset of providers. The system identifies a provider based on analysis of the information and the healthcare data and provides trial program criteria for analysis at a provider system. The provider system has access to healthcare data for subjects that interact with the provider. The computing system generates data indicating a result of screening each subject by analyzing the trial program criteria against healthcare data for each subject and receives data for a selection of a subject from the provider system. The selection is determined using screening data for the subject. A referral network of the system provides the screening data for access and analysis at an investigator system.
G16H 10/20 - ICT specially adapted for the handling or processing of patient-related medical or healthcare data for electronic clinical trials or questionnaires
G16H 10/60 - ICT specially adapted for the handling or processing of patient-related medical or healthcare data for patient-specific data, e.g. for electronic patient records
94.
Enabling data flow in an electronic referral network
Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for a computing system that identifies information about a trial program. The information is related to healthcare data for a subset of providers. The system identifies a provider based on analysis of the information and the healthcare data and provides trial program criteria for analysis at a provider system. The provider system has access to healthcare data for subjects that interact with the provider. The computing system generates data indicating a result of screening each subject by analyzing the trial program criteria against healthcare data for each subject and receives data for a selection of a subject from the provider system. The selection is determined using screening data for the subject. A referral network of the system provides the screening data for access and analysis at an investigator system.
G16H 10/20 - ICT specially adapted for the handling or processing of patient-related medical or healthcare data for electronic clinical trials or questionnaires
G16H 10/60 - ICT specially adapted for the handling or processing of patient-related medical or healthcare data for patient-specific data, e.g. for electronic patient records
95.
System and method to regularize cancer treatment data for systematic recording
Implementations provide a method to consolidate data records of regimens for treating oncology conditions. The method includes: accessing data records each encoding multi-tier data characteristics of a regimen for treating a particular oncology condition; receiving a first data record encoding a first regimen specific to a first healthcare provider institution; parsing the first data record according to a hierarchy of the encoded multi-tier data characteristics; distributing a respective weight to each of the encoded data characteristics to account for the potentially missing data characteristic; comparing data characteristics of the first data record with data characteristics from the data records by applying the respective weight to each data characteristic at a particular tier of the hierarchy such that a respective compound score is generated for each data record; and based on the compound scores for all data records, determining a prevailing data record of regimen as matching the first data record.
G16H 70/20 - ICT specially adapted for the handling or processing of medical references relating to practices or guidelines
G16H 20/10 - ICT specially adapted for therapies or health-improving plans, e.g. for handling prescriptions, for steering therapy or for monitoring patient compliance relating to drugs or medications, e.g. for ensuring correct administration to patients
G06F 16/25 - Integrating or interfacing systems involving database management systems
G16H 50/70 - ICT specially adapted for medical diagnosis, medical simulation or medical data miningICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for mining of medical data, e.g. analysing previous cases of other patients
96.
UNBIASED ETL SYSTEM FOR TIMED MEDICAL EVENT PREDICTION
An unbiased ETL (extract, transform, load) system for timed medical event prediction utilizes a rolling series of time-bound cross-sections of patient healthcare data. Patients may be labelled as belonging to one or more classes (e.g. positive or negative) for each cross-section in the series depending on current healthcare status. Rather than using a single snapshot, the unbiased ETL system employs multiple snapshots of patient medical histories to provide a capability to classify a patient at different points in time, as appropriate. Supervised learning for the system is thereby enabled over multiple different periods of a patient's medical journey which advantageously supports a more statistically robust medical event prediction model and eliminates several classes of bias. Additionally, the unbiased ETL system enables customization of a prediction window to account for lags in data collection, data processing, and length of use of the medical event predictions.
G16H 50/50 - ICT specially adapted for medical diagnosis, medical simulation or medical data miningICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for simulation or modelling of medical disorders
G16H 10/60 - ICT specially adapted for the handling or processing of patient-related medical or healthcare data for patient-specific data, e.g. for electronic patient records
G06F 16/25 - Integrating or interfacing systems involving database management systems
A computer-implemented tool for automated classification and interpretation of documents, such as life science documents supporting clinical trials, is configured to perform a combination of raw text, document construct, and image analyses to enhance classification accuracy by enabling a more comprehensive machine-based understanding of document content. The combination of analyses provides context for classification by leveraging relative spatial relationships among text and image elements, identifying characteristics and formatting of elements, and extracting additional metadata from the documents as compared to conventional automated classification tools, wherein natural language processing (NLP) is applied to associate text with tokens, and relevant differences and similarities between protocols are identified.
A computer-assisted method to provide timely multi-channel notification of treatments to healthcare providers and patients, the method including receiving de-identified longitudinal medical records, treatment prescription records of healthcare providers, and notification data. Relationships between the healthcare providers, the anonymized patients, and the notifications are identified using the de-identified longitudinal medical records, the treatment prescription records of the healthcare providers, and the notification data. An impact of notifications being received by both the healthcare provider for the anonymized patient and the anonymized patient on whether the anonymized patient received the treatment is determined. A plan to timely provide notifications of treatments to the healthcare provider and the anonymized patients is determined based at least on the impact of the notifications being received by both the healthcare provider for the anonymized patient and the anonymized patient on whether the anonymized patient received the treatment.
G16H 20/10 - ICT specially adapted for therapies or health-improving plans, e.g. for handling prescriptions, for steering therapy or for monitoring patient compliance relating to drugs or medications, e.g. for ensuring correct administration to patients
G16H 80/00 - ICT specially adapted for facilitating communication between medical practitioners or patients, e.g. for collaborative diagnosis, therapy or health monitoring
G16H 10/60 - ICT specially adapted for the handling or processing of patient-related medical or healthcare data for patient-specific data, e.g. for electronic patient records
99.
Automated classification and interpretation of life science documents
A computer-implemented tool for automated classification and interpretation of documents, such as life science documents supporting clinical trials, is configured to perform a combination of raw text, document construct, and image analyses to enhance classification accuracy by enabling a more comprehensive machine-based understanding of document content. The combination of analyses provides context for classification by leveraging relative spatial relationships among text and image elements, identifying characteristics and formatting of elements, and extracting additional metadata from the documents as compared to conventional automated classification tools.
One example method for predictive clinical planning and design includes instantiating a plurality of data objects, each data object of the plurality of data objects comprising clinical trial information; displaying a graphical user interface on one or more display screens, the graphical user interface providing a graphical representation of at least a portion of a clinical trial and comprising a plurality of graphical nodes; receiving a selection of the second graphical node; receiving, via an editor associated with the second graphical node, a modification of the second data object; propagating an indication of the modification to the first data object, the propagation modifying a clinical trial data item of the first data object; and displaying, within the first graphical node, the modified clinical trial data item of the first data object.
G06T 11/20 - Drawing from basic elements, e.g. lines or circles
G16H 10/20 - ICT specially adapted for the handling or processing of patient-related medical or healthcare data for electronic clinical trials or questionnaires
G16H 10/60 - ICT specially adapted for the handling or processing of patient-related medical or healthcare data for patient-specific data, e.g. for electronic patient records
G16H 40/63 - ICT specially adapted for the management or administration of healthcare resources or facilitiesICT specially adapted for the management or operation of medical equipment or devices for the operation of medical equipment or devices for local operation
