Predicting "Rare" events in an ICU

We are developing a prediction system that predicts rare events like hypotensive episodes in an ICU setting. We have assembled a large arterial blood pressure feature-level dataset from a publicly available waveform dataset. One of the challenges is that the balance of the classes in the data is extremely skewed due to the rare nature of the events we are interested in. This imbalance in the data can significantly impact the accuracy of the forecast and it especially affects the dynamics of our iterative learning engine. The goal of the project is to develop and identify an efficient method to balance the data and techniques that could address this problem within our framework.

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: hembergerik@csail.mit.edu, hembergerik@csail.mit.edu, kalyan@csail.mit.edu or unamay@csail.mit.edu