In intensive care units, 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. By training on patients grouped by health status, the neural network can better estimate if patients will die in the hospital.

Mortality 

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.

Patients Admitted ICU Patients

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.

Patients At Risk 

ICUs are very high-bandwidth, with a lot of patients. It is important to figure out well ahead of time which patients are actually at risk and in more need of immediate attention.

Multitasking and patient subpopulations

A key innovation of the work is that, during training, the model separates patients into distinct subpopulations, which captures aspects of a patient’s overall state of health and mortality risks. It does so by calculating a combination of physiological data, broken down by the hour. 

Next, the model employs a multitasking method of learning to build predictive models. When the patients are broken into subpopulations, differently tuned models are assigned to each subpopulation. Each variant model can then more accurately make predictions for its personalized group of patients. 

Patients Admitted To ICU

Patients admitted to the ICU often differ in why they’re there and what their health status is like. Because of this, they’ll be treated very differently. Clinical decision-making aids “should account for the heterogeneity of these patient populations and make sure there is enough data for accurate predictions.

Such performance disparities are difficult to measure without evaluating by subpopulations; they want to evaluate how well our model does, not just on a whole cohort of patients, but also when we break it down for each cohort with different medical characteristics. That can help researchers in better predictive model training and evaluation.

Mortality Rate

Two subpopulations, for instance, contained patients with elevated blood pressure over the first several hours but one decreased over time, while the other maintained the elevation throughout the day. This subpopulation had the highest mortality rate.

Using those subpopulations, the model predicted the mortality of the patients over the following 48 hours with high specificity and sensitivity and various other metrics. The multitasking model significantly outperformed a global model by several percentage points.

Treatments

The researchers aim to use more data from electronic health records, such as treatments the patients are receiving. They also hope, in the future, to train the model to extract keywords from digitized clinical notes and other information.