Publication

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

Christian Mandel, Kathrin Stich, Serge Autexier, Christoph Lüth, Ariane Ziehn, Karin Hochbaum, Rolf Dembinski, Christoph Int-Veen

In: Proceedings of the 44th. International Conference of the IEEE Engineering in Medicine and Biology Society. International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC-2022) Biomedical Engineering transforming the provision of healthcare: promoting wellness through personalized & predictable provision at the point of care July 11-15 Glasgow United Kingdom IEEE Xplore 2022.

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.

Projekte

German Research Center for Artificial Intelligence
Deutsches Forschungszentrum für Künstliche Intelligenz