Abstract: Disclosed herein are methods and systems for implementing a multi-model computer architecture for entity identification. A method includes receiving data regarding a plurality of entities. The method includes generating a plurality of entity profiles for the plurality entities and a network graph data structure (e.g., a node graph) comprising edges between nodes for the plurality of entity profiles. The method includes executing a model using identifiers of the plurality of entity profiles, an event topic, and the edges between the nodes as input to generate one or more composite scores for the plurality of entity profiles. The method includes selecting one or more entities for the event based on the generated one or more composite scores. The method includes generating a record comprising associations between identifications of the entities and the event.

Abstract: Disclosed herein are methods and systems for filtering entity profiles. A method includes receiving, from a plurality of data sources and for a plurality of medical entities, clinical data and online interaction data; generating a plurality of entity profiles for the plurality medical entities; calculating one or more clinical metrics and one or more online interaction metrics for the plurality of entity profiles from the clinical data and the online interaction data; executing a model using identifications of the plurality of entity profiles and the one or more clinical metrics and the one or more online interaction metrics as input to generate influence scores for the plurality of entity profiles; selecting a subset of the plurality of entity profiles responsive to each entity profile of the subset having an influence score satisfying a selection criteria; and generating a record comprising identifications of the subset of the plurality of entity profiles.

Abstract: Disclosed herein are methods and systems for implementing a machine learning architecture for detecting early adopters. A method includes receiving clinical data; training, using a supervised or unsupervised learning technique, a machine learning model (e.g., a neural network, a support vector machine, or a random forest) to generate a score identifying a likelihood to prescribe a medical product; generating, by the machine learning model, a score indicating a likelihood of a first medical personnel to prescribe a type of medical product within a defined time period; and causing a display at a client device based on the score.