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Dr. Nigam Shah is Professor of Medicine at Stanford University, and Chief Data Scientist for Stanford Health Care. His research group analyzes multiple types of health data (EHR, Claims, Wearables, Weblogs, and Patient blogs), to answer clinical questions, generate insights, and build predictive models for the learning health system. At Stanford Healthcare, he leads artificial intelligence and data science efforts for advancing the scientific understanding of disease, improving the practice of clinical medicine and orchestrating the delivery of health care. Dr. Shah is an inventor on eight patents and patent applications, has authored over 200 scientific publications and has co-founded three companies. Dr. Shah was elected into the American College of Medical Informatics (ACMI) in 2015 and was inducted into the American Society for Clinical Investigation (ASCI) in 2016. He holds an MBBS from Baroda Medical College, India, a PhD from Penn State University and completed postdoctoral training at Stanford University.
We analyze multiple types of health data (EHR, Claims, Wearables, Weblogs, and Patient blogs), to answer clinical questions, generate insights, and build predictive models for the learning health system. Read more at http://shahlab.stanford.eduWe answer clinical questions to enable better medical decisions using EHR and Claims data, via a bedside consult service that enables the use of aggregate patient data at the point of care. See more at http://greenbutton.stanford.eduWe make predictions that allow taking mitigating actions. We characterize the fairness and examine the ethical implications of using machine learning in clinical care. We have built models for predicting future increases in cost, identifying slow healing wounds, missed diagnoses of depression and for improving palliative care. Learn more at http://aihc.stanford.eduWe develop methods to analyze multiple datatypes for generating insights. Such as:(1) Combining molecular data with EHR data to identify biomarkers for poor outcomes in fibrotic diseases.(2) Learning effective treatment pathways in Type 2 Diabetes using medical claims data from multiple countries.(3) Monitoring Point-of-Care glucose meters using coincident testing with central laboratory measurements.(4) Detecting skin adverse reactions by analyzing content in a health social network. (5) Conducting surveillance for drug adverse events and safety of medical devices by analyzing using clinical notes in the EHR.(6) Mining Web search logs to predict health utilization and analyzing information seeking behavior of health professionals.(7) Inferring physical function from wearables data.(8) Assessing the accuracy of automatic speech recognition for psychotherapy.