We combine machine learning, text-mining, and prior knowledge in medical ontologies to discover hidden trends, build risk models, and drive comparative effectiveness studies to enable 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.
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. Our efforts in drug safety surveillance were recently the focus of a commentary titled Advancing the Science of Pharmacovigilance.
Learning Health System examples:
Data mining for drug safety:
Effectiveness of large datasets and simple methods:
Current Group: Lab members
Open Positions: Health Services Research Postdoctoral Fellow | Informatics Postdoctoral Fellow
Data Science Fellow
Internal (log in required): Lab information, Projects, Rotations, Archived pages
On Boarding: New Lab members, For Collaborators
BIOMEDIN 215 Data Driven Medicine Autumn quarter of each year
Medicine in the Age of Electronic Health Records, at KDD 2014 in New York.