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.
Answering clinical questions
Insights from data
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.
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.
Building a Machine Learning Healthcare System, at XLDB 2018
Performing an Informatics Consult, Grand rounds in Medicine at Stanford, Feb 1 2017
Seminars on campus