Pete's Birthday Conference!|
Nov 21, 2019
Pete has had a huge impact both on the field and on all of us, and that's something worth celebrating. We hope you can join us at this event to celebrate Pete, meet your extended academic family, and learn what everyone is working on — and, of course, hear some great stories about Pete! Please check here for more details!
Several 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!
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.