Dr. Shah's lab develops methods to annotate, index and analyze large unstructured datasets for enabling use cases of the learning health system. Their research group is part of the Center for Biomedical Informatics Research at Stanford and the National Center for Biomedical Ontology.
They 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. They 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. They 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.
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