- Professor of Computer Science and Engineering, Massachusetts Institute of Technology
Peter Szolovits is Professor of Computer Science and Engineering and head of the Clinical Decision-Making Group within CSAIL. His research centers on the application of AI methods to problems of medical decision making and 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, and privacy and confidentiality issues in medical record systems.
Professor Szolovits’ interests in AI include knowledge representation, qualitative reasoning, and probabilistic inference. His interests in medical computing include Web-based heterogeneous medical record systems, life-long personal health information systems, and design of cryptographic schemes for health identifiers. He teaches classes in artificial intelligence, programming languages, medical computing, medical decision making, knowledge-based systems, computer systems engineering and probabilistic inference.
Prof. Szolovits has served on journal editorial boards and as program chairman and on the program committees of national conferences. He has been a founder of and consultant for several companies that apply AI to problems of commercial interest. He received his bachelor’s degree in physics and his PhD in information science, both from Caltech. Prof. Szolovits was elected to the National Academy of Medicine (formerly the Institute of Medicine) of the National Academies and is a Fellow of the American Association for Artificial Intelligence, the American College of Medical Informatics, and the American Institute for Medical and Biological Engineering. He recently served as a member of the National Research Council’s Computer Science and Telecommunications Board and also served as a member of the National Library of Medicine’s Biomedical Library and Informatics Review Committee.
- PhD in Information Science, Caltech
- BS in Physics, Caltech
- American College of Medical Informatics: Morris F. Collen Award of Excellence, 2013
- Harvard Medical School Center for Biomedical Informatics: Innovation in Personally Controlled Health Record Infrastructure, 2006
- Member, Institute of Medicine of the National Academies, 2005
- Fellow, American Institute for Medical and Biological Engineering, 2005
- NASA: Space Act Award, 1995
- Fellow, Association for the Advancement of Artificial Intelligence, 1992
- Fellow, American College of Medical Informatics, 1984
Professor Szolovits’ research interests broadly include much of biomedical informatics. Throughout his career, he has avoided a tightly focused concentration on a single topic. Instead, he has tried to define his research interests by the demands of health care and how they could be satisfied by computing approaches. The Clinical Decision Making Group is dedicated to exploring and furthering the application of technology and artificial intelligence to clinical situations. Because of the vital and crucial nature of medical practice, and the need for accurate and timely information to support clinical decisions, the group is also focused on the gathering, availability, security and use of medical information throughout the human “life cycle” and beyond.
- S. Yu, K. P. Liao, S. Y. Shaw, V. S. Gainer, S. E. Churchill, P. Szolovits, et al. “Toward high-throughput phenotyping: unbiased automated feature extraction and selection from knowledge sources.” Journal of the American Medical Informatics Association (2015): 1-10.
- Y. Luo, Y. Xin, E. Hochberg, R. Joshi, Ö. Uzuner, and P. Szolovits. “Subgraph Augmented Non-Negative Tensor Factorization (SANTF) for Modeling Clinical Narrative Text.” Journal of the American Medical Informatics Association (2015).
- M. Ghassemi, M. A. F. Pimentel, T. Naumann, T. Brennan, D. A. Clifton, P. Szolovits, and M. Feng. “A Multivariate Timeseries Modeling Approach to Severity of Illness Assessment and Forecasting in ICU with Sparse, Heterogeneous Clinical Data.” AAAI 2015 Proceedings (2015): 446-53.
- K. P. Liao, T. Cai, G. K. Savova, S. N. Murphy, E. W. Karlson, A. N. Ananthakrishnan, et al. “Development of phenotype algorithms using electronic medical records and incorporating natural language processing.” BMJ (Clinical Research Ed) (2015): 350.
- M. Ghassemi, T. Naumann, F. Doshi-Velez, N. Brimmer, R. Joshi, A. Rumshisky, and P. Szolovits. “Unfolding Physiological State: Mortality Modelling in Intensive Care Units.” KDD: Proceedings/International Conference on Knowledge Discovery and Data Mining (2014): 75-84.
A full list of Professor Szolovits publications can be found on his website.