American Statistical Association
New York City
Metropolitan Area Chapter

Levin Lecture Series: Fall 2019 Colloquium Seminars
Department of Biostatistics
Mailman School of Public Health
Columbia University



DISTRIBUTED LEARNING FROM
MULTIPLE EHRS DATABASES FOR PREDICTING MEDICAL EVENTS

by

Dr. Qi Long
Professor, Biostatistics, Perelman School of Medicine
University of Pennsylvania

Host: Yuanjia Wang


Abstract

Electronic health records (EHRs) data offer great promises in personalized medicine. However, EHRs data also present significant analytical challenges due to their irregularity and complexity. For example, EHRs include data from multiple domains collected over time and include both structured and unstructured data. In addition, analyzing EHR data involves privacy issues and sharing such data across multiple institutions/sites may be infeasible. Building on a contextual embedding model, we propose a distributed learning approach to learn from multiple EHRs databases and predict multiple medical events simultaneously, which can handle both structured and unstructured data. We further augment the proposed approach with Differential Privacy to enhance privacy protection. Our numerical studies demonstrate that the proposed method can build predictive models in a distributed fashion with privacy protection and the resulting models achieve reasonable prediction accuracy compared with methods that use pooled data across all sites. Our algorithm, if integrated into EHR system as a decision support tool, has the potential to improve early detection and diagnosis of diseases which is known to be associated with better patient outcomes.

This is joint work with Ziyi Li, Kirk Roberts, and Xiaoqian Jiang.


Date: Thursday, October 24, 2019
Time: 11:30 A.M. - 12:30 P.M.
Location: Mailman School of Public Health
Department of Biostatistics
722 West 168th Street
AR Building
8th Floor Auditorium
New York, New York

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