Learning health knowledge bases

David Sontag / MIT

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Abstract: How can we use AI to help with medical diagnosis of rare conditions from symptoms? How can we organize a patient’s longitudinal health record into problem-oriented views? Surface subtle medical errors? Summarize long clinical documents? I will argue that developing algorithms for these tasks that are safe, robust, and easy for clinicians and patients to use will require building on top of large health knowledge bases of clinical entities, relations, and their grounding in human physiology. I will then describe our work over the past several years developing methods for learning health knowledge bases directly from health data of millions of patients, and finish with a series of challenges that the field must solve for us to ultimately achieve this vision.

Bio: David Sontag is an Associate Professor in the Department of Electrical Engineering and Computer Science (EECS) at MIT, and member of the Institute for Medical Engineering and Science (IMES) and the Computer Science and Artificial Intelligence Laboratory (CSAIL). Prior to joining MIT, Dr. Sontag was an Assistant Professor in Computer Science and Data Science at New York University from 2011 to 2016, and a postdoctoral researcher at Microsoft Research New England. Dr. Sontag received the Sprowls award for outstanding doctoral thesis in Computer Science at MIT in 2010, best paper awards at the conferences Empirical Methods in Natural Language Processing (EMNLP), Uncertainty in Artificial Intelligence (UAI), and Neural Information Processing Systems (NeurIPS), faculty awards from Google, Facebook, and Adobe, and a National Science Foundation Early Career Award. Dr. Sontag received a B.A. from the University of California, Berkeley.