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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. Our work sometimes gets covered in the popular news as well.


We use longitudinal EHR data, including unstructured data, for three groups of projects: answering clinical questions, generating insights, and building predictive models.

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 up to 12 months before the diagnosis of depression.
  • Rapid identification of slow healing wounds: We demonstrate that it is possible to build a model for identifying delayed healing wounds with an Area Under the Curve (AUC) of 0.842 that works across all wound types.
  • Implications of non-stationarity on predictive modeling using EHRs: Under the non-stationarity in the underlying dataset, the performance advantage of complex methods such as stacking relative to the best simple classifier disappears. Ignoring non-stationarity can thus lead to sub-optimal model selection in predictive modeling tasks.

Phenotypic profiling:

Group information


BIOMEDIN 215 Data Driven Medicine Autumn quarter of each year



start.txt · Last modified: 2016/01/05 17:11 by nigam