Using Gated Recurrent Unit Networks for the Prediction of Hemodynamic and Pulmonary Decompensation

Abstract

This paper presents a new medical severity scoring system, used to assess the risk of hemodynamic and pulmonary decompensation for patients being treated in intensive care units. The score presented here includes drug circulatory support and ventilation mode data for the evaluation of the patient's biosignals and laboratory values. It is shown that Gated Recurrent Unit-based neural networks are able to predict the maximal severity class within a 24 hour prediction time-frame (hemodynamic: 0.85 AUROC / pulmonary: 0.9 AUROC), and can estimate the underlying decompensation score for prediction times of up to 24 hours with mean errors of 6.3% of the maximal possible pulmonary, and 9.6% of the hemodynamic score. These results are based on 60h observation period. Clinical Relevance - Hemodynamic and pulmonary decom-pensation are life threatening dynamic events that can lead to death of patients. Early detection of these incidents is essential in order to intervene therapeutically and to improve survival chances. In everyday intensive care physicians are confronted with a vast number of laboratory values and vital parameters. There is a risk that early stages of hemodynamic and pulmonary decompensation are misjudged. The implementation of robust warning systems could support physicians in detecting these critical events and initiate therapeutical intervention in time which would achieve significant reduction of patient mortality