Leveraging Multiple Data Sources for Clinical Prediction Modeling

Friday, November 20, 2020 -
2:00pm to 3:00pm
Zoom
Elsie Ross, M.D., Ph.D.

Assistant Professor of Surgery (Vascular Surgery) and of Medicine (BMIR)

Stanford University Medical Center

Leveraging Multiple Data Sources for Clinical Prediction Modeling

Abstract: 

Though there has been a large influx of rich biomedical data in recent years, what data is useful for our uses cases and how we integrate different data types into a cohesive prediction model is still an open area of research and includes a lot of trial and error. In this talk Dr. Ross will discuss her research in progress regarding efforts to integrate genetic, clinical, and imaging data for vascular disease risk prediction.

Bio: 

Dr. Elsie Ross is a vascular surgeon scientist whose lab focuses on using big data and advanced analytics to improve care of the vascular patient. Her current research focuses on using electronic health records and machine learning to automate the identification of patients with vascular disease and to recommend guideline-based therapies.