Abstract: The application includes systems and methods for phenotyping. A request is received to identify a target population having a phenotype. Predefined characteristics associated with the phenotype are obtained. Using a retriever model, a set of subjects is identified as potential members of the target population by searching databases. Medical information is obtained by searching a second set of one or more databases and providing corresponding information to an artificial intelligence (AI) component. Natural language instructions are provided to the AI component to provide context to the AI component to determine whether a respective subject in the set of subjects has at least one of the one or more predefined characteristics. A subset of subjects is identified by the AI system to be, or to have a high likelihood of being, a member of the target population.

Abstract: Systems and methods for phenotyping clinical data are provided. The method includes obtaining episodic records comprising unstructured clinical data from an electronic medical record (EMR) or electronic health record (EHR) for patients. The method also includes filtering the episodic records by language pattern recognition to identify episodic records that each includes an expression related to a clinical condition. The method also includes splitting each episodic record to obtain snippets comprising tokens. The method also includes predicting if an episodic record represents an instance of the clinical condition using a trained classifier. The trained classifier includes an aggregation function that aggregates the snippets to output a corresponding representation for the episodic record, and an interpretation function that interprets the corresponding representation to output a corresponding prediction for whether the episodic record represents an instance of the clinical condition.