Electronic medical record phenotyping using the anchor and learn framework
, MIT CSAIL
Date: Wednesday, May 03, 2017
Time: 11:30 AM to 1:00 PM Note: all times are in the Eastern Time Zone
Refreshments: 11:15 AM
Host: Bonnie Berger
Contact: Patrice Macaluso, 617-253-3037, firstname.lastname@example.org
Speaker URL: None
email@example.com, firstname.lastname@example.org, email@example.com
TALK: Electronic medical record phenotyping using the anchor and learn framework
Electronic medical records (EMRs) hold a tremendous amount of information about patients that is relevant to determining the optimal
approach to patient care. As medicine becomes increasingly precise, a patients electronic medical record phenotype will play an important
role in triggering clinical decision support systems that can deliver personalized recommendations in real time. In this talk, I introduce our recently developed "anchor and learn" framework for efficient lylearning statistically driven phenotypes with minimal manual intervention. Using this approach, we developed a phenotype library that uses both structured and unstructured data from the EMR to
represent patients for real-time clinical decision support. The resulting phenotypes are interpretable and fast to build. Evaluated in
an emergency department setting, we find that our semi-supervised learning approach (which uses no manually labeled data) performs comparably to supervised learning.
Based on joint work with Yoni Halpern and Steven Horng.
Created by Patrice Macaluso at Thursday, April 20, 2017 at 11:41 AM.