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We analyze multiple data types (EHR, Claims, Wearables, Weblogs, and Patient blogs), to answer clinical questions, generate insights, and build predictive models at the Stanford Center for Biomedical Informatics Research. We use machine learning, text-mining, and prior knowledge in medical ontologies to enable the learning health system.

Group information

Current Group: Lab members
On Boarding: New Lab members, For Collaborators
Internal (log in required): Lab information, Lab communication, Projects, Rotations, Archived pages
Contact: Nigam Shah



Answering clinical questions

Insights from data

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.
  • Predicting patient ‘cost blooms’: We develop models that identify new entrants to the upper decile of per capita healthcare expenditures in the next year.

Selected Talks

Building a Machine Learning Healthcare System, at XLDB 2018

Performing an Informatics Consult, Grand rounds in Medicine at Stanford, Feb 1 2017

Other talks

Seminars on campus

start.txt · Last modified: 2018/07/25 10:04 by nigam