BP-Watch: Predicting blood pressure in an ICU setting

We are building a large scale predictive system that predicts the blood pressure for a patient under intensive care. The project relies on cloud-scale machine learning of many diverse predictive models. A variety of tasks are on the agenda including cloud-scale empirical experimentation, cross-referencing model predictions to clinical events, time series modeling, unsupervised learning of similar blood pressure segments and ultimately transforming many model outputs which are in the form of probabilities and predictions into visualizations that are succinct and informative to the doctors and intensivists who will use this system. This is an exciting project which focuses on making machine learning matter in the real world scenarios and creating impact. You will work with a team of doctors, post-docs and graduate students.

MEng, Juniors and Seniors looking to lead to MEng via 6.UAT, UAP
Background: Course 6 courses in software and machine learning knowledge (6.034 and 6.867) 
Please contact: kalyan@csail.mit.edu or unamay@csail.mit.edu