We use 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.
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. News coverage.
Learning Health System examples:
Insights from data mining:
Effectiveness of large datasets and simple methods:
Current Group: Lab members
Open Positions: Informatics Postdoctoral Fellow
Data Science Fellow, Health Services Research Postdoctoral Fellow
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On Boarding: New Lab members, For Collaborators
BIOMEDIN 215 Data Driven Medicine Autumn quarter of each year