In Digital health
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.
Kessler Steven, Schroeder Dennis, Korlakov Sergej, Hettlich Vincent, Kalkhoff Sebastian, Moazemi Sobhan, Lichtenberg Artur, Schmid Falko, Aubin Hug
2023
Digital health, deep learning, intensive care unit, readmission prediction, time-series analysis
