Papers got accepted by Machine Learning for Health (ML4H) workshop at NeurIPS 2019 |
Oct 8, 2019
Our group members have several full papers and extended abstracts got accepted by the NeurIPS 2019 ML4H workshop. Please visit us in Vancouver!
[Course] Machine Learning for Healthcare (6.S897, HST.956) in Spring 2019|
Jan 1, 2019
Pete and Prof. David Sontag will co-teach the new course!
Yuan Luo [PhD 2015] is recognized by an AMIA dissertation award|
Dec 1, 2017
Dr. Yuan Luo, who completed his doctoral dissertation in our group, has won Honorable Mention (2nd prize) in the inagural American Medical Informatics Association (AMIA) Doctoral Dissertation Award competition. His thesis title is “Towards Unified Biomedical Modeling with Subgraph Mining and Factorization Algorithms". His thesis was co-supervised by Prof. Szolovits, who heads MEDG, and Prof. Özlem Uzuner of SUNY/Albany, who is a Research Affiliate. Yuan is now an Assistant Professor of Health and Biomedical Informatics at Northwestern University.
The Clinical Decision Making Group at the MIT Laboratory for Computer Science is a research group 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.
We collaborate closely with doctors and biomedical scientists. We derive knowledge from the growing set of patients' health records that note the natural histories of diseases and the outcomes of clinical care interventions. Such models can predict outcomes in new cases depending on what actions are chosen. Optimizing such choices enables recommendations for care that has the highest expected benefit.
As part of our approach, we use natural language processing methods to extract meaningful data from the clinical narratives that contain most observations made by doctors, nurses and other specialists. We apply a variety of machine learning techniques, including deep learning, matrix and tensor factorization, Gaussian processes, support vector machines, conditional random fields, logistic regression, Bayesian models, random forests, and reinforcement learning, and adapt them to the peculiar characteristics of clinical data. These include irregular sampling of data and observations, data that are not missing at random, narratives that are full of duplication, ambiguities and telegraphic abbreviations and elisions, data recorded by multiple instruments and people that are not well calibrated, etc.
We also work on related issues such as using personal health information systems to engage patients and families in care, augmenting classical clinical data sets with data from wearable instrumentation, social media reports, environmental exposures and the vastly expanding sets of genetic and genomic data. We have also contributed to advances in protecting patient privacy while allowing use of records for research, development of more flexible and supportive informational tools and interfaces, use of speech to interact with systems, etc.