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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:

Predictive Modeling

  • Predicting Diagnoses of Depression: We developed a model that uses electronic medical record (EMR) data for predicting the diagnosis of depression. Our model achieved an area under the ROC curve of 0.82 to 0.84 and can predict up to 12 months before the diagnosis of depression.

Phenotypic profiling:

Effectiveness of large datasets and simple methods:

Group information


BIOMEDIN 215 Data Driven Medicine Autumn quarter of each year



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