Machine Learning Pilot for Electronic Phenotyping of Health Outcomes of Interest

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Details

Basic Details

Date Posted:

Wednesday, November 14, 2018

Status:

Complete

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.

Deliverable(s) (1)

Machine Learning Pilot for Electronic Phenotyping

Additional Information

Information

Time Period:

2016 - 2017

HOI Study Type:

Novel Approaches to More Efficient Outcome Validation

Population / Cohort:

All relevant records from the IBM Watson Health Claims EMR Database will be extracted for patients meeting the HOI inclusion criteria developed during the study

Data Source(s):

IBM Watson Health Claims EMR Database

Contributors

Workgroup Leader(s):

Jenna Wong, PhD, MSc; Darren Toh, ScD; Department of Population Medicine, Harvard Pilgrim Health Care Institute and Harvard Medical School, Boston, MA

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

Michael Nguyen, MD; Center for Drug Evaluation and Research, FDA, Silver Spring, MD

Workgroup Member(s):

James Williams, MBA; Department of Population Medicine, Harvard Pilgrim Health Care Institute and Harvard Medical School, Boston, MA

Emma Mollenhauer, BS; David Lewandowski; Timothy Burrell, MD, MBA; Shannon Harrer; IBM Watson Health, Ann Arbor, MI

Sai Dharmarajan, PhD, MS; Wei Hua, PhD, MHS, MS; Rita Ouellet-Hellstrom, PhD, MPH; Elande Baro, PhD, MS; Robert Ball, MD, MPh; Center for Drug Evaluation and Research, FDA, Silver Spring, MD

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Methods, Data, & Tools

Machine Learning Pilot for Electronic Phenotyping of Health Outcomes of Interest

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