Electronic phenotyping with APHRODITE and the Observational Health Sciences and Informatics (OHDSI) data network

Abstract

The widespread usage of electronic health records (EHRs) for clinical research has produced multiple electronic phenotyping approaches. Methods for electronic phenotyping range from those needing extensive specialized medical expert supervision to those based on semi-supervised learning techniques. We present Automated PHenotype Routine for Observational Definition, Identification, Training and Evaluation (APHRODITE), an R package phenotyping framework that combines noisy labeling and anchor learning. APHRODITE makes these cutting-edge phenotyping approaches available for use with the Observational Health Data Sciences and Informatics (OHDSI) data model for standardized and scalable deployment. APHRODITE uses EHR data available in the OHDSI Common Data Model to build classification models for electronic phenotyping. We demonstrate the utility of APHRODITE by comparing its performance versus traditional rule-based phenotyping approaches. Finally, the resulting phenotype models and model construction workflows built with APHRODITE can be shared between multiple OHDSI sites. Such sharing allows their application on large and diverse patient populations.

Publication
Proceedings of the AMIA Summit on Clinical Research Informatics (CRI)
Yoni Halpern
Yoni Halpern
PhD student

Google Brain

David Sontag
David Sontag
Associate Professor of EECS

My research focuses on advancing machine learning and artificial intelligence, and using these to transform health care.

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