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.

Speaker: Dr. Nigam Shah, Stanford