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We develop methods to annotate, index and analyze large unstructured datasets for enabling use cases of the learning health system. Our research group is part of the Center for Biomedical Informatics Research at Stanford and the National Center for Biomedical Ontology. Press coverage of our work can be found in Forbes, GigaOM, Science News, EHR Intelligence and the Stanford Medicine magazine.

Data driven medicine: We combine machine learning, text-mining, and prior knowledge in medical ontologies to discover hidden trends, build risk models, drive data driven decision making, and comparative effectiveness studies. We have shown that using unstructured data, it is possible to monitor for adverse drug events, learn drug-drug interactions, identify off-label drug usage, generate practice-based evidence for difficult-to-test clinical hypotheses, identify new medical insights, and generate phenotypic fingerprints as well as build predictive models. We have efforts around combining multiple information sources for drug safety surveillance, which were recently the focus of a commentary titled Advancing the Science of Pharmacovigilance.

Annotation Analytics: In order to understand the “gene lists” from analysis of high-throughput data, researchers routinely use Gene Ontology based analyses. With available methods for automated annotation and the existence of over 200 biomedical ontologies, we can stop using just GO and move to enrichment analysis using disease ontologies.

Our Group: Lab members
Open Positions: Postdoc position | Data Science Fellow
Internal (log in required): Lab information, Projects, Onboarding, Rotations


BIOMEDIN 215 Data Driven Medicine Autumn quarter of each year


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