Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.