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 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 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 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 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 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 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 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 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 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: 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.