Dr. Nigam Shah is Assistant Professor of Biomedical Informatics at Stanford University, Assistant Director of the Center for Biomedical Informatics Research, and a core member of the Biomedical Informatics Graduate Program.

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 patient notes into a de-identified patient-feature matrix. We will then review four use-cases, which use the resulting de-identified data matrix, to illustrate the learning of practice-based evidence from unstructured data in electronic medical records. By examining patterns, such as the frequency and co-frequency of drug and disease mentions, it is possible to monitor for adverse drug events, learn drug-drug interactions, profile the safety of off-label drug usage, uncover ‘natural experiments’ and generate practice-based evidence for difficult-to-test clinical hypotheses.

An RSVP is requested for this event:
http://dlhs-umi.ch/apr29nigamshah-rsvp.