MLxMED: Machine Learning in Medicine Seminar

  • Remote Access Enabled - Zoom Webinar
  • Virtual Presentation
  • DAVID SONTAG
  • Associate Professor
  • Department of Electrical Engineering and Computer Science (EECS)
  • Massachusetts Institute of Technology
Seminars

Going Beyond Diagnosis and Prognosis: Machine Learning to Guide Treatment Suggestions

The next decade will see a shift in focus of machine learning in healthcare from models for diagnosis and prognosis to models that directly guide treatment decisions. We introduce methods for learning treatment policies from electronic medical records and demonstrate their use in learning to recommend antibiotics for women with uncomplicated urinary tract infections. Our methods can take into consideration multiple factors, e.g. efficacy, cost, risk of complications, that should be optimized when learning policies. We show how to perform policy distillation, after learning, to simplify deployments. We introduce the concept of a 'target deployment' to guide retrospective evaluation, showing how this can be used to obtain fair comparisons to existing clinical practice. We find that, relative to clinicians, our models reduce inappropriate antibiotic prescriptions from 11.9% to 9.5% while at the same time using 50% fewer second-line antibiotics. Finally, we discuss mistakes that we made and lessons learned.

Based on joint work with Sooraj Boominathan, Michael Oberst, Helen Zhou, and Sanjat Kanjilal (BWH/MGH).

David Sontag joined the MIT faculty in 2017 as Hermann L. F. von Helmholtz Career Development Professor in the Institute for Medical Engineering and Science (IMES) and as Associate Professor in the Department of Electrical Engineering and Computer Science (EECS). He is also a principal investigator in the Computer Science and Artificial Intelligence Laboratory (CSAIL). Professor Sontag's research interests are in machine learning and artificial intelligence. As part of IMES, he leads a research group that aims to transform healthcare through the use of machine learning.

Prior to joining MIT, Dr. Sontag was an Assistant Professor in Computer Science and Data Science at New York University's Courant Institute of Mathematical Sciences from 2011 to 2016, and postdoctoral researcher at Microsoft Research New England from 2010 to 2011. 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 (NIPS), faculty awards from Google, Facebook, and Adobe, and a NSF CAREER Award. Dr. Sontag received a B.A. from the University of California, Berkeley.

About the MLxMED Series: Medicine is complex and data-driven while discovery and decision making are increasingly enabled by machine learning. Machine learning has the potential to support, enable and improve medical discovery and clinical decision making in areas such as medical imaging, cancer diagnostics, precision medicine, clinical trials, and electronic health records. This seminar series focuses on new algorithms, real-world deployment, and future trends in machine learning in medicine. It will feature prominent investigators who are developing and applying machine learning to biomedical discovery and in clinical decision support.

Zoom Participation. See announcement.

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