Zurück zur Übersicht

Predicting readmission to the cardiovascular intensive care unit using recurrent neural networks

full text: html PDF
author/s: Steven Kessler, Dennis Schroeder, Sergej Korlakov, Vincent Hettlich, Sebastian Kalkhoff, Sobhan Moazemi, Arthur Lichtenberg, Falko Schmid, Hug Aubin
type:Article
journal:DIGITAL HEALTH
volume:9
month:January
year:2023
ISSN:2055-2076
Abstract

If a patient can be discharged from an intensive care unit (ICU) is usually decided by the treating physicians based on their clinical experience. However, nowadays limited capacities and growing socioeconomic burden of our health systems increase the pressure to discharge patients as early as possible, which may lead to higher readmission rates and potentially fatal consequences for the patients. Therefore, here we present a long short-term memory-based deep learning model (LSTM) trained on time series data from Medical Information Mart for Intensive Care (MIMIC-III) dataset to assist physicians in making decisions if patients can be safely discharged from cardiovascular ICUs. To underline the strengths of our LSTM we compare its performance with a logistic regression model, a random forest, extra trees, a feedforward neural network and with an already known, more complex LSTM as well as an LSTM combined with a convolutional neural network. The results of our evaluation show that our LSTM outperforms most of the above models in terms of area under receiver operating characteristic curve. Moreover, our LSTM shows the best performance with respect to the area under precision-recall curve. The deep learning solution presented in this article can help physicians decide on patient discharge from the ICU. This may not only help to increase the quality of patient care, but may also help to reduce costs and to optimize ICU resources. Further, the presented LSTM-based approach may help to improve existing and develop new medical machine learning prediction models.

Heinrich Heine Universität

Datenbanken und Informationssysteme

Lehrstuhlinhaber

Prof. Dr. Stefan Conrad


Universitätsstr. 1
40225 Düsseldorf
Gebäude: 25.12
Etage/Raum: 02.24
Tel.: +49 211 81-14088

Sekretariat

Sabine Freese


Sprechzeiten:
Mo-Fr: 10:00-11:30 Uhr
Mo-Do: 13:00-14:30 Uhr


Universitätsstr. 1
40225 Düsseldorf
Gebäude: 25.12
Etage/Raum: 02.22
Tel.: +49 211 81-11312
Fax: +49 211 81-13463
Verantwortlich für den Inhalt:  E-Mail senden Datenbanken & Informationssysteme