Machine Learning Pilot for Electronic Phenotyping of Health Outcomes of Interest

Project Title Machine Learning Pilot for Electronic Phenotyping of Health Outcomes of Interest
Date Posted
Thursday, November 15, 2018
Status
In progress
Description

The aim of this project is to demonstrate the feasibility and efficiency of developing and validating of a claims-based health outcome interest (HOI) algorithm using machine learning classification techniques applied to a linked claims-electronic medical records (EMR) database. This project has the potential to improve the electronic phenotype development and validation process for outcomes that can be detected via standardized information in an EMR to accelerate validation of claims-based signatures.

Workgroup Leader(s)

Jenna Wong PhD, MSc; Judy Maro PhD, MS; Department of Population Medicine, Harvard Pilgrim Health Care Institute and Harvard Medical School, Boston, MA

Teresa B. Gibson PhD; IBM Watson Health, Ann Arbor, MI

Workgroup Members

Jeffrey Brown PhD; Nicolas Beaulieu MA; Darren Toh ScD; James Williams MBA; Department of Population Medicine, Harvard Pilgrim Health Care Institute and Harvard Medical School, Boston, MA

Robert Ball MD, MPH, ScM; Center for Drug Evaluation and Research, FDA, Silver Spring, MD

Time Period
2016 - 2017
Data Sources
IBM Watson Health Claims EMR Database