Abstract: Methods, systems, and non-transitory computer readable mediums are disclosed for recommending medically-relevant articles. One method comprises receiving health data associated with a patient, storing the health data in a data structure as historical patient data, identifying new appointment scheduling data associated with the patient, determining an appointment subject associated with the new appointment scheduling data, determining a correlation between the new appointment scheduling data and the historical patient data, searching one or more databases for one or more relevant articles, retrieving the one or more relevant articles, generating a recommendation for the one or more relevant articles, and presenting the recommendation for the one or more relevant articles to a user through at least one of a news feed, a calendar, or an electronic medical record (EMR) associated with the patient.

Abstract: Various embodiments of the present disclosure provide methods, apparatus, systems, computing devices, computing entities, and/or the like for multi-node system monitoring using one or more distributed ledgers, wherein a ledger entry request received from a miner node computing entity is validated based at least in part on executing a hierarchical validation workflow that comprises an ordered sequence of L validation iterations, wherein each of one or more ith non-initial validation iterations is performed based at least in part on an association of an (i?1)th validation iteration with a non-affirmative validation determination.

Abstract: This disclosure describes techniques that include a method for estimating healthcare costs, the method comprising applying, by a computing system, a machine learning (ML) model to a user subgraph of a current user to generate an estimated healthcare cost of the current user for a future time period, wherein the user subgraph of the current user is graph data comprising nodes and edges that represent information associated with medical care of the current user; and generating, by the computing system, a user interface including a budget for the future time period that includes the estimated healthcare cost and a list of selectable medical expense categories, wherein each of the selectable medical expense categories includes an associated cost.

Abstract: Embodiments of the present disclosure provide methods, apparatus, systems, computing devices, computing entities, and/or the like for processing an inclusion of an entity for an event. In accordance with one embodiment, a method is provided that includes: determining whether a graph representation data object comprises an inbound edge connecting an entity node representing the entity with an event node representing the event; and responsive to determining the graph representation data object comprises the inbound edge, performing an action involving inclusion of the entity for the event. The inbound edge is generated via an inbound edge generator machine learning model configured to: traverse entity and/or inclusion edges of the graph representation data object to identify inclusion and entity edges connected, generate an entity score data object for the entity based at least in part on the inclusion edges, and responsive to the data object satisfying a threshold, generate the inbound edge.

Abstract: Various embodiments of the present invention provide methods, apparatus, systems, computing devices, computing entities, and/or the like for performing predictive data analysis operations. For example, certain embodiments of the present invention utilize systems, methods, and computer program products that perform predictive data analysis operations by using a graph-based recurrence classification machine learning framework that includes a graph neural network machine learning model and a recurrence classification machine learning model, where the recurrence classification machine learning model is configured to generate a predicted recurrence classification based at least in part on one or more graph-based features generated by a graph neural network machine learning model and one or more entity features associated with an entity identifier for an incoming event.

Abstract: Embodiments of the present disclosure provide methods, apparatus, systems, computing devices, computing entities, and/or the like for processing an inclusion of an entity for an event. In accordance with one embodiment, a method is provided that includes: determining whether a graph representation data object comprises an inbound edge connecting an entity node representing the entity with an event node representing the event; and responsive to determining the graph representation data object comprises the inbound edge, performing an action involving inclusion of the entity for the event. The inbound edge is generated via an inbound edge generator machine learning model configured to: traverse entity and/or inclusion edges of the graph representation data object to identify inclusion and entity edges connected, generate an entity score data object for the entity based at least in part on the inclusion edges, and responsive to the data object satisfying a threshold, generate the inbound edge.

Abstract: Embodiments of the present disclosure provide methods, apparatus, systems, computing devices, computing entities, and/or the like for processing an inclusion of an entity for an event. In accordance with one embodiment, a method is provided that includes: determining whether a graph representation data object comprises an inbound edge connecting an entity node representing the entity with an event node representing the event; and responsive to determining the graph representation data object comprises the inbound edge, performing an action involving inclusion of the entity for the event. The inbound edge is generated via an inbound edge generator machine learning model configured to: traverse entity and/or inclusion edges of the graph representation data object to identify inclusion and entity edges connected, generate an entity score data object for the entity based at least in part on the inclusion edges, and responsive to the data object satisfying a threshold, generate the inbound edge.