Development of a machine learning model for the prediction of the short-term mortality in patients in the intensive care unit.

Purpose: The aim of this study was to develop and evaluate a machine learning model that predicts short-term mortality in the intensive care unit using the trends of four easy-to-collect vital signs.

Materials And Methods: The primary training cohort included 1968 patients at the Veterans Health Service Medical Center. The external validation cohort comprised 409 patients at Seoul National University Hospital. Datasets of heart rate, systolic blood pressure, diastolic blood pressure, and peripheral capillary oxygen saturation (SpO2) measured every hour for 10 h were used. The performances of mortality prediction models generated using five machine learning algorithms, Random Forest (RF), XGboost, perceptron, convolutional neural network, and Long Short-Term Memory, were calculated and compared using area under the receiver operating characteristic curve (AUROC) values and an external validation dataset.

Results: The machine learning model generated using the RF algorithm showed the best performance. Its AUROC was 0.922, which is much better than the 0.8408 of the Acute Physiology and Chronic Health Evaluation II. The machine learning model developed using SpO2 showed the best performance (AUROC, 0.89).

Conclusions: This simple yet powerful new mortality prediction model could be useful for early detection of probable mortality and appropriate medical intervention, especially in rapidly deteriorating patients.