Machine Learning in Health Care: Too Important to Be a Toy Example

Sherri Rose / Harvard Medical School

Abstract: The massive size of the health care sector make data science applications in this space particularly salient for social policy. An overarching theme of this keynote is that developing machine learning methodology tailored to specific substantive health problems and the associated electronic health data is critical given the stakes involved, rather than eschewing complexity in simplified scenarios that may no longer represent an actual real-world problem.

Bio: Sherri Rose, Ph.D. is an Associate Professor of Health Care Policy at Harvard Medical School and Co-Director of the Health Policy Data Science Lab. Her research in health policy focuses on risk adjustment, comparative effectiveness, and health program evaluation. Dr. Rose coauthored the first book on machine learning for causal inference and has published work across fields, including in Biometrics, JASA, PMLR,Journal of Health Economics, and NEJM. She currently serves as co-editor of the journal Biostatistics and is Chair-Elect of the American Statistical Association’s Biometrics Section. Her honors include the ISPOR Bernie J. O’Brien New Investigator Award for exceptional early career work in health economics and outcomes research and an NIH Director’s New Innovator Award to develop machine learning estimators for generalizability in health policy.

Presentation (SlidesLive)