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We are a group of about 12 doctors, engineers, informatics professionals and students focused on enabling better care using existing health data. We develop novel methods to learn from patient-level health data, answer clinical questions that enable better medical decisions at the point of care, and have an active effort to research safe, ethical, and cost-effective strategies for using predictive models to guide mitigating care actions. Our research group is part of the Department of Medicine at Stanford, the Clinical Excellence Research Center, and the Department of Biomedical Data Science.


We analyze multiple types of health data (EHR, Claims, Wearables, Weblogs, and Patient blogs), in service of the learning health system (see examples). The work can be grouped into three focus areas:

  1. We develop methods to analyze multiple datatypes for generating insights such as detecting skin adverse reactions by analyzing content in a health social network, enabling medical device surveillance, discovering drug adverse events from clinical notes using novel methods for processing textual documents.
  2. We answer clinical questions using aggregate patient data at the bedside. The green button project established the viability of this idea and led to the creation of Atropos Health.
  3. We build predictive models that allow taking mitigating actions, keeping the human in the loop. Research on foundation models from our team is put into practice by the Data Science team at SHC.

About us: Lab members, Open positions, Blogs and media
Internal (GSuite log in via SunetID): Compute & Data Resources, Group communication


  • BIOMEDIN 215, taught for the BMI Graduate program is designed to prepare you to pose and answer meaningful clinical questions using routinely collected healthcare data.
  • BIOMEDIN 225, taught for the MCiM program explores how to use electronic health records (EHRs) and other patient data in conjunction with recent advances in artificial intelligence (AI) and evolving business models to improve healthcare.
  • AI in Healthcare Specialization on Coursera, which reviews the current and future applications of AI in healthcare with the goal of learning to bring AI technologies into the clinic safely and ethically.
  • XBIOMEDIN215 Online, where you work through interactive exercises and case studies, attend live webinars, receive ongoing feedback from the course team, and collaborate with your fellow learners to gain the real-world skills doing machine learning projects.
  • AI in Healthcare Bootcamp, provides students an opportunity to do cutting-edge research at the intersection of AI and healthcare.
  • Miscellaneous Talks


start.txt · Last modified: 2024/07/18 14:46 by nigam