Abstract: Methods, systems, and computer-readable media for linking multiple data entities. The method collects a snapshot of data from one or more data sources and converts it into a canonical representation of records expressing relationships between data elements in the records. The method next cleans the records to generate output data of entities by grouping chunks of records using a machine learning model. The method next ingests the output data of entities to generate a versioned data store of the entities and optimizes versioned data store for real-time data lookup. The method then receives a request for data pertaining to a real-world entity and presenting relevant data from the versioned data store of entities.

Abstract: Computer-implemented systems and methods are disclosed for deploying documents in a live environment. The systems and methods can provide a configuration environment including a testing environment and a staging environment that can be used to configure documents that can implement software as a system. The documents can provide users with various services, that can be accessed by the documents in a testing staging environment and a live environment. The documents can be used to edit configuration files that can correspond with an entity or a patient. After being edited, a diff utility can be used to calculate and provide differences between a modified configuration file and an original configuration file. Non-transitory computer readable storage media for storing instructions that use the methods are also disclosed.

Abstract: Methods, systems, and computer-readable media for updating a machine learning model utilized in a search engine operation. The method identifies a set of search queries in stored search queries corresponding to a service and apply the identified set of search queries to the search engine to generate one or more search results for the service. Each search result has an assigned aggregate based on values of a set of parameters associated with the service. The method then analyzes the values of the set of parameters to determine a tradeoff point of each parameter to determine one or more weights to apply to the machine learning model based on the tradeoff points. The method stores the determined one or more weights and applies them to the machine learning model for a search query corresponding to the service.

Abstract: Methods, systems, and computer-readable media for linking multiple data entities. The method collects a snapshot of data from one or more data sources and converts it into a canonical representation of records expressing relationships between data elements in the records. The method next cleans the records to generate output data of entities by grouping chunks of records using a machine learning model. The method next ingests the output data of entities to generate a versioned data store of the entities and optimizes versioned data store for real-time data lookup. The method then receives a request for data pertaining to a real-world entity and presenting relevant data from the versioned data store of entities.

Abstract: Methods, systems, and computer-readable media for generating a personalized action recommendation are provided. The method acquires a request for a service that is associated with a user and the user's condition. The method then identifies one or more features of the user based on stored user information. The method next assigns the user to a segment based on the identified one or more features, generates a set of one or more recommended actions for the user based on the segment, and determines an expected value of each of the one or more recommended actions. The method determines a rank of the one or more recommended actions based on the expected value of each of the one or more recommended actions, and outputs a recommended action with a highest expected value for the user in response to the request for the service.

Abstract: Computer-implemented systems and methods are disclosed for deploying documents in a live environment. The systems and methods can provide a configuration environment including a testing environment and a staging environment that can be used to configure documents that can implement software as a system. The documents can provide users with various services, that can be accessed by the documents in a testing staging environment and a live environment. The documents can be used to edit configuration files that can correspond with an entity or a patient. After being edited, a diff utility can be used to calculate and provide differences between a modified configuration file and an original configuration file. Non-transitory computer readable storage media for storing instructions that use the methods are also disclosed.

Abstract: Methods, systems, and computer-readable media for updating a machine learning model utilized in a search engine operation. The method identifies a set of search queries in stored search queries corresponding to a service and apply the identified set of search queries to the search engine to generate one or more search results for the service. Each search result has an assigned aggregate based on values of a set of parameters associated with the service. The method then analyzes the values of the set of parameters to determine a tradeoff point of each parameter to determine one or more weights to apply to the machine learning model based on the tradeoff points. The method stores the determined one or more weights and applies them to the machine learning model for a search query corresponding to the service.

Abstract: Methods, systems, and computer-readable media for generating a statistically covaried machine learning model for performance measurement of service providers. The method receives a configuration file that includes one or more parameters associated with a plurality of individuals and parses it to generate and executing the database query on input data to generate sets of tabulated data of individuals of the plurality of individuals. The method next determines one or more measures of service providers listed in the configuration file using two or more tabulated data of individuals from the sets of tabulated data of individuals. The method finally generates a covaried machine learning model by training a machine learning model by statistically covarying measures and using them as training data.

Abstract: Methods, systems, and computer-readable media for linking multiple data entities. The method collects a snapshot of data from one or more data sources and converts it into a canonical representation of records expressing relationships between data elements in the records. The method next cleans the records to generate output data of entities by grouping chunks of records using a machine learning model. The method next ingests the output data of entities to generate a versioned data store of the entities and optimizes versioned data store for real-time data lookup. The method then receives a request for data pertaining to a real-world entity and presenting relevant data from the versioned data store of entities.

Abstract: Methods, systems, and computer-readable media for determining the expertise of service providers to match with users utilizing a service provider search system. The method identifies searched conditions and determine associated codes. The method next determines procedures provided by service providers associated with codes. The method then normalizes codes associated with conditions and selects a subset of them based on the popularity of procedures associated with the codes. The method finally utilizes a machine learning model to translate the subset of codes to topics and calculates similarity metric between the topics and the service providers and tunes the threshold of the metric. The method then an using the tuned threshold outputs a service provider based on a query to a service provider search system.

Abstract: Systems and methods are provided for data driven predictive analysis of complex data sets for determining decision outcomes. The systems and methods include obtaining a first set of data associated with individuals and obtaining, a second set of data associated with events, wherein the events are associated with at least one of the individuals. The systems and methods further include determining a subset of the individuals from the first set of data based on the first subset of individuals having common attributes with a target individual and determining a second subset of events from the second set of data based on the second subset of events having common attributes with the target events associated with the target individual. Additionally, the systems and methods include aggregating data associated with the second subset of events based on the target events and providing for display output associated with aggregated data.

Abstract: Methods, systems, and computer-readable media for generating a personalized action recommendation are provided. The method acquires a request for a service that is associated with a user and the user's condition. The method then identifies one or more features of the user based on stored user information. The method next assigns the user to a segment based on the identified one or more features, generates a set of one or more recommended actions for the user based on the segment, and determines an expected value of each of the one or more recommended actions. The method determines a rank of the one or more recommended actions based on the expected value of each of the one or more recommended actions, and outputs a recommended action with a highest expected value for the user in response to the request for the service.

Abstract: Methods, systems, and computer-readable media for updating a machine learning model utilized in a search engine operation. The method identifies a set of search queries in stored search queries corresponding to a service and apply the identified set of search queries to the search engine to generate one or more search results for the service. Each search result has an assigned aggregate based on values of a set of parameters associated with the service. The method then analyzes the values of the set of parameters to determine a tradeoff point of each parameter to determine one or more weights to apply to the machine learning model based on the tradeoff points. The method stores the determined one or more weights and applies them to the machine learning model for a search query corresponding to the service.

Abstract: Systems and methods for dynamic data processing and graphical user interface generation are provided. A system may include a network interface configured to request and receive, via a computer network from one or more sources in remote locations, electronic record data associated with an individual; an input filter configured to identify structured and unstructured information in the electronic record data; a data selector configured to analyze the structured and unstructured information; a timeline generator configured to generate, based on the analysis, interface information for displaying an interactive graphical user interface configured to present an event timeline of events in the electronic record data; and a display configured to provide the interactive graphical user interface based on the generated interface information.

Abstract: Systems and methods are provided for data driven predictive analysis of complex data sets for determining decision outcomes. The systems and methods include obtaining a first set of data associated with individuals and obtaining, a second set of data associated with events, wherein the events are associated with at least one of the individuals. The systems and methods further include determining a subset of the individuals from the first set of data based on the first subset of individuals having common attributes with a target individual and determining a second subset of events from the second set of data based on the second subset of events having common attributes with the target events associated with the target individual. Additionally, the systems and methods include aggregating data associated with the second subset of events based on the target events and providing for display output associated with aggregated data.

Abstract: Methods, systems, and computer-readable media for generating a virtual based on location. The method acquires a request for a service based on a type of service and is associated with a user, the user's location, and user preferences. The method then acquires a set of service providers based on the type of service and the user's location who are filtered from a larger set of service providers using user preferences. The method in the next step acquires a machine learning model that is based on stored information associated with other users travel patterns and with service providers providing the service and the geographical information associated with the user's location. The method executed the identified machine learning model to aggregate a subset of service providers based on output from the machine learning model. The machine learning model is inputted the set of service providers, the user's location, the user's preferences, and the geographical information.

Abstract: Aspects of the present invention relate to system and methods for assigning quality scores to one or more caregivers, such as physicians. In embodiments, a ranking or score may be based, at least in part, upon a combination of quality scores from one or more stages in a physician's academic training and clinic practice and based, at least in part, upon the quality of peers of that physician at various stages in the career progression of the physician. This information may be used to help a potential patient identify a physician for their care.

Abstract: Methods, systems, and computer-readable media for generating a personalized virtual network. The method acquires a request for a service associated with a user and their preferences. The method then identifies one or more conditions of the user and their propensities based on a first set of machine learning models using stored past information of the user. The method next identifies a second set of machine learning models and evaluates the weightage of each model based on the determined propensities of the conditions. The evaluated weights are applied to the second set of models to generate a personalized virtual network for the user.

Abstract: Computer-implemented systems and methods are disclosed for data driven expertise mapping. The systems and methods provide for obtaining data sets from data sources, wherein the data sets include services related data, analyzing the data sets, wherein the analysis generates information representative of the services related data, and generating training sets related to the data sets, wherein the training sets are based on known values. The systems and methods further provide for generating models, wherein the models are based on determining services provided by service providers using a combination of the services related data, the analysis of the data sets and the training sets, and provide a mapping of at least one service to service providers. The systems and methods additionally include evaluating the models based on known values and storing an indication for providing to a graphical user interface based on more models.

Abstract: Systems and methods are provided for data driven analysis, modeling, and semi-supervised machine learning for qualitative and quantitative determinations. The systems and methods include obtaining data associated with individuals, and determining features associated with the individuals based on the data and similarities among the individuals based on the features. The systems and methods can label some individuals as exemplary, generate a graph wherein nodes of the graph represent individuals, edges of the graph represent similarity among the individuals, and nodes associated labeled individuals are weighted. The disclosed system and methods can apply a weight to unweighted nodes of the graph based on propagating the labels through the graph where the propagation is based on influence exerted by the weighted nodes on the unweighted nodes. The disclosed systems and methods can provide output associated with the individuals represented on the graph and the associated weights.