In intensive care units (ICUs), where patients come in with a wide range of health conditions, triaging relies heavily on clinical judgment. ICU staff run numerous physiological tests, such as bloodwork and checking vital signs, to determine if patients are at immediate risk of dying if not treated aggressively.

Enter machine learning. Numerous models have been developed in recent years to help predict patient mortality in the ICU, based on various health factors during their stay. These models, however, have performance drawbacks. One common type of “global” model is trained on a single large patient population. These might work well on average, but poorly on some patient subpopulations. On the other hand, another type of model analyzes different subpopulations—for instance, those grouped by similar conditions, patient ages, or hospital departments—but often have limited data for training and testing.

In a paper recently presented at the Proceedings of Knowledge Discovery and Data Mining conference, MIT researchers describe a machine-learning model that functions as the best of both worlds: It trains specifically on patient subpopulations, but also shares data across all subpopulations to get better predictions. In doing so, the model can better predict a patient’s risk of mortality during their first 2 days in the ICU, compared to strictly global and other models.

The model first crunches physiological data in electronic health records of previously admitted ICU patients, some who had died during their stay. In doing so, it learns high predictors of mortality, such as low heart rate, high blood pressure, and various lab test results—high glucose levels and white blood cell count, among others—over the first few days and breaks the patients into subpopulations based on their health status. Given a new patient, the model can look at that patient’s physiological data from the first 24 hours and, using what it’s learned through analyzing those patient subpopulations, better estimate the likelihood that the new patient will also die in the following 48 hours.

Moreover, the researchers found that evaluating (testing and validating) the model by specific subpopulations also highlights performance disparities of global models in predicting mortality across patient subpopulations. This is important information for developing models that can more accurately work with specific patients.

“ICUs are very high-bandwidth, with a lot of patients,” says first author Harini Suresh, a graduate student in the Computer Science and Artificial Intelligence Laboratory (CSAIL). “It’s important to figure out well ahead of time which patients are actually at risk and in more need of immediate attention.”

Coauthors on the paper are CSAIL graduate student Jen Gong, and John Guttag, the Dugald C. Jackson Professor in Electrical Engineering.