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: Methods, systems, and computer-readable media for multi-domain, multi-modal data segmentation, and automatically generating and refining hypotheses. The method receives data from a plurality of data sources; synthesizing the receive data; identifying trigger event data based on the synthesized data; generating an episode based on a segmentation of the synthesized data and trigger event data; and identifying at least one set of observational features associated with the episode based on the synthesized data and a relevancy metric. The method also includes iteratively generating a hypothesis based on the observational features using machine learning, predicting an outcome based on the hypothesis using machine learning, generating an outcome measure, and validating the hypothesis based on the outcome measure.

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: 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 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 multi-domain, multi-modal data segmentation, and automatically generating and refining hypotheses. The method receives data from a plurality of data sources; synthesizing the receive data; identifying trigger event data based on the synthesized data; generating an episode based on a segmentation of the synthesized data and trigger event data; and identifying at least one set of observational features associated with the episode based on the synthesized data and a relevancy metric. The method also includes iteratively generating a hypothesis based on the observational features using machine learning, predicting an outcome based on the hypothesis using machine learning, generating an outcome measure, and validating the hypothesis based on the outcome measure.

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 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 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 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 multi-domain, multi-modal data segmentation, and automatically generating and refining hypotheses. The method receives data from a plurality of data sources; synthesizing the receive data; identifying trigger event data based on the synthesized data; generating an episode based on a segmentation of the synthesized data and trigger event data; and identifying at least one set of observational features associated with the episode based on the synthesized data and a relevancy metric. The method also includes iteratively generating a hypothesis based on the observational features using machine learning, predicting an outcome based on the hypothesis using machine learning, generating an outcome measure, and validating the hypothesis based on the outcome measure.

Abstract: Methods, systems, and computer-readable media for the generation of customizable insights using machine learning models. The method acquires data from one or more sources with different formats and data organization to extract events that each represent an interaction between a user and a service and classify the extracted events by adding labels to each of the extracted events. The method next projects the extracted events and generates the customized insights of events representing user interactions.

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 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: 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 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 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.

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 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.