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Contact: Nigam Shah

• BIOMEDIN 215 Data Driven Medicine Autumn quarter of each year
• AI in Healthcare Bootcamp

• A ‘Green Button’ For Using Aggregate Patient Data At The Point Of Care: We suggest a “green button” function within EHRs for clinicians to use aggregate patient data for real time decision making at the point of care. Check out the idea, or our recently launched Informatics Consult Service that puts this idea in action.
• Profiling risk factors for chronic uveitis in juvenile idiopathic arthritis: We report a new association between allergic conditions and chronic uveitis. Covered in Stanford Medicine Mag – Great Medical Mines and in the Wall Street Journal
• Proton Pump Inhibitor Usage and the Risk of Myocardial Infarction in the General Population: This study provides an example of how a combination of experimental studies and data-mining approaches can be applied to prioritize drug safety signals for further investigation.See coverage in NPR, Washington Post, NY Times, Forbes, KQED, Fox News, and Scientific American.
• Androgen Deprivation Therapy and Future Alzheimer's Disease Risk: This study found an association between the use of ADT in the treatment of prostate cancer and an increased risk of Alzheimer's disease in a general population cohort.

• Pharmacovigilance using clinical notes: Uses textual clinical notes for detecting single drug–adverse event associations (AUC of 80.4%) and for detecting drug–drug interactions (AUC of 81.5%). Press in Forbes, GigaOM. Our efforts were the focus of an editorial commentary titled Advancing the Science of Pharmacovigilance.
• Information Seeking and Drug-Safety Alert Response by Health Care Professionals: The information-seeking behavior associated with online evidence sources can offer insight into the information needs of health professionals and enable large-scale medical surveillance.
• An unsupervised learning method to identify reference intervals from a clinical database: We show that it is possible to use laboratory results and coded diagnoses to learn laboratory test reference intervals from clinical data warehouses.
• Postmarket Surveillance of Point-of-Care Glucose Meters through Analysis of Electronic Medical Records: We show that it is possible to assess device accuracy using coincident testing of point-of-care and central laboratory blood glucose measurements in a large cohort of critically ill patients. Editorial in Clinical Chemistry.

• 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.

Building a Machine Learning Healthcare System, at the Parc Forum, Summer 2016

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

Other talks

Seminars