Mortality prediction among ICU inpatients based on MIMIC-III database results from the conditional medical generative adversarial network

Wei Yang; Hong Zou; Meng Wang; Qin Zhang; Shadan Li; Hongyin Liang

doi:10.1016/j.heliyon.2023.e13200

Mortality prediction among ICU inpatients based on MIMIC-III database results from the conditional medical generative adversarial network

Heliyon. 2023 Jan 24;9(2):e13200. doi: 10.1016/j.heliyon.2023.e13200. eCollection 2023 Feb.

Authors

Wei Yang¹, Hong Zou^{2

3}, Meng Wang⁴, Qin Zhang⁵, Shadan Li¹, Hongyin Liang²

Affiliations

¹ Department of Urology, The General Hospital of Western Theater Command (Chengdu Military General Hospital), Chengdu, 610083, China.
² Department of General Surgery, The General Hospital of Western Theater Command (Chengdu Military General Hospital), Chengdu, 610083, China.
³ Department of Liver Surgery & Liver Transplantation, State Key Laboratory of Biotherapy and Cancer Center, West China Hospital, Sichuan University and Collaborative Innovation Center of Biotherapy, Chengdu 610044, Sichuan Province, China.
⁴ Department of Traditional Chinese Medicine, The General Hospital of Western Theater Command (Chengdu Military General Hospital), Chengdu, 610083, China.
⁵ Department of Gastroenterology, The 77th Army Hospital, Jiajiang, 614100, China.

Abstract

Background and aims: Improved mortality prediction among intensive care unit (ICU) inpatients is a valuable and challenging task. Limited clinical data, especially with appropriate labels, are an important element restricting accurate predictions. Generative adversarial networks (GANs) are excellent generative models and have shown great potential for data simulation. However, there have been no relevant studies using GANs to predict mortality among ICU inpatients. In this study, we aim to evaluate the predictive performance of a variant of GAN called conditional medical GAN (c-med GAN) compared with some baseline models, including simplified acute physiology score II (SAPS II), support vector machine (SVM), and multilayer perceptron (MLP).

Methods: Data from a publicly available intensive care database, the Medical Information Mart for Intensive Care III (MIMIC-III) database (v1.4), were included in this study. The area under the precision-recall curve (PR-AUC), area under the receiver operating characteristic curve (ROC-AUC), and F1 score were used to evaluate the predictive performance. In addition, the size of the dataset was artificially reduced, and the performance of the c-med GAN was compared in different size datasets.

Results: The results showed that c-med GAN achieves the best PR-AUC, ROC-AUC, and F1 score compared with SAPS II, SVM, and MLP when training in the full MIMIC-III dataset. When the size of the dataset was reduced, the prediction performances of both MLP and c-med GAN were affected. However, the c-med GAN still outperformed MLP on smaller datasets and had less degradation.

Conclusion: The prediction of in-hospital mortality based on the c-med GAN for ICU patients showed better performance than the baseline models. Despite some inadequacies, this model may have a promising future in clinical applications which will be explored by further research.

Keywords: C-med GAN; GAN; MIMIC-III; Mortality prediction; ROC-AUC.