Uncovering Clinically Relevant Medical Knowledge

The day-to-day practice of medicine is based largely on a combination of the personal experience of those making the decisions and non-patient-specific information derived by applying conventional statistical methods to large clinical trials. With the boom in the collection of clinical information in computationally accessible formats, it is now possible to use advanced machine learning and data mining techniques to put clinical decision making on a sounder more patient-specific basis. That is the mission of CSAIL's Data -driven Medical Research Group. Current projects include risk stratification post acute coronary syndrome, prediction of impending heart failure, diagnosis of mental disease from EEG data, and understanding ways to reduce the prevalence of healthcare associated infections.