Peter Szolovits

Peter Szolovits is Professor of Computer Science and Engineering in the MIT Department of Electrical Engineering and Computer Science (EECS) and an Associate faculty member in the MIT Institute of Medical Engineering and Science (IMES) and its Harvard/MIT Health Sciences and Technology (HST) program. He is also head of the Clinical Decision-Making Group within the MIT Computer Science and Artificial Intelligence Laboratory (CSAIL). His research centers on the application of AI methods to problems of medical decision making, natural language processing to extract meaningful data from clinical narratives, and the design of information systems for health care institutions and patients. He has worked on problems of diagnosis, therapy planning, execution and monitoring for various medical conditions, computational aspects of genetic counseling, controlled sharing of health information, privacy and confidentiality issues in medical record systems, and integration of clinical and genomic data for translational medicine. His interests in AI include knowledge representation, qualitative reasoning, probabilistic inference, and machine learning. He teaches classes in artificial intelligence, programming languages, computer systems engineering, medical computing, medical decision making, knowledge-based systems and probabilistic inference.

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An introduction to machine learning for healthcare, ranging from theoretical considerations to understanding human consequences of deploying technology in the clinic, through hands-on Python projects using real healthcare data. Machine learning methods have revolutionized many aspects of healthcare, from new models that help clinicians make more informed decisions to [...]

Reinforcement Learning

Understanding Diseases

Clinical Care

Healthcare

Machine Learning