Building a Machine Learning Healthcare System
In the era of Electronic Health Records (EHR), it is possible to examine the outcomes of decisions made by doctors during clinical practice to identify patterns of care-generating evidence from the collective experience of millions of patients. We will discuss methods that transform EHR data into a de-identified, temporally ordered, patient-feature matrix. We will review use cases, which use the resulting de-identified data, to discover hidden trends, build predictive models, and drive comparative effectiveness studies in a learning health system. We will also discuss the notion of an “Informatics consult” service to make use of such practice-based evidence in clinical care.
Dr. Nigam Shah is associate professor of Medicine (Biomedical Informatics) at Stanford University, Assistant Director of the Center for Biomedical Informatics Research, and a core member of the Biomedical Informatics Graduate Program. Dr. Shah's research focuses on combining machine learning and prior knowledge in medical ontologies to enable use cases of the learning health system.
Dr. Shah received the AMIA New Investigator Award for 2013 and the Stanford Biosciences Faculty Teaching Award for outstanding teaching in his graduate class on “Data-driven medicine.” Dr. Shah was elected into the American College of Medical Informatics (ACMI) in 2015 and inducted into the American Society for Clinical Investigation (ASCI) in 2016. He holds an MBBS from Baroda Medical College, India, a Ph.D from Penn State University and completed postdoctoral training at Stanford University.
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