Abstract: In the era of Electronic Health Records, 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 patients. We will discuss methods that transform unstructured EHR data into a de-identified, temporally ordered, patient-feature matrix. We will also review use-cases, which use the resulting de-identified data, for pharmacovigilance, to reposition drugs, build predictive models, and drive comparative effectiveness studies in a learning health system.
Lunch will be served from 12:30-1pm, on a first-come, first-served basis. The talk will begin promptly at 1pm.
Presenter Bio: 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.
Free and open to the public; no registration required.