Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for enhancing communications between a client device and AI/ML models. In some implementations, a method includes a system that receives multiple requests for machine learning models. The system determines whether a first request corresponds to other requests. In response to determining the first request corresponds to one or more of the other requests, the system provides, to a first user device that submitted the first request, a natural language response previously produced by the machine learning models associated with the first request. In response to determining that a second request does not correspond to any of the other requests, the system provides, to the machine learning models, the second request. The system provides, to a second user device that submitted the second request, output from the machine learning models that processed the second request.

Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for enhancing communications between a client device and AI/ML models. In some implementations, a method includes a system that receives multiple requests for machine learning models. The system determines whether a first request corresponds to other requests. In response to determining the first request corresponds to one or more of the other requests, the system provides, to a first user device that submitted the first request, a natural language response previously produced by the machine learning models associated with the first request. In response to determining that a second request does not correspond to any of the other requests, the system provides, to the machine learning models, the second request. The system provides, to a second user device that submitted the second request, output from the machine learning models that processed the second request.

Abstract: Methods and systems including computer programs encoded on computer storage media, for a method for detecting signals related to subjects participating in a clinical trial. In some implementations, a computer collects clinical data from multiple sources. The computer standardizes and redacts personally identifiable information and determines predictive features that correspond to characteristics of the data that correlate with efficacy and safety signals. The computer obtains training data and trains machine learning models to predict one or both of a predicted efficacy signal and a predicted safety signal for a subject. The computer receives clinical data for a particular clinical trial subject enrolled in an ongoing clinical trial and predicts one or more signals and receives a review of the signals through a user interface. The computer updates the predictive components of the system based on the review.

Abstract: 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.

Abstract: 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).

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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).

Abstract: 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.

Abstract: 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.

Abstract: 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).

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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).