Repository of Research and Investigative Information

Repository of Research and Investigative Information

Ilam University of Medical Sciences

Design of an artificial neural network to predict mortality among COVID-19 patients

Tue Jul 23 20:36:57 2024

(2022) Design of an artificial neural network to predict mortality among COVID-19 patients. Informatics in medicine unlocked. p. 100983. ISSN 2352-9148 (Print) 2352-9148 (Linking)

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Introduction: The fast pandemic of coronavirus disease 2019 (COVID-19) has challenged clinicians with many uncertainties and ambiguities regarding disease outcomes and complications. To deal with these uncertainties, our study aimed to develop and evaluate several artificial neural networks (ANNs) to predict the mortality risk in hospitalized COVID-19 patients. Material and methods: The data of 1710 hospitalized COVID-19 patients were used in this retrospective and developmental study. First, a Chi-square test (P < 0.05), Eta coefficient (eta > 0.4), and binary logistics regression (BLR) analysis were performed to determine the factors affecting COVID-19 mortality. Then, using the selected variables, two types of feed-forward (FF) models, including the back-propagation (BP) and distributed time delay (DTD) were trained. The models' performance was assessed using mean squared error (MSE), error histogram (EH), and area under the ROC curve (AUC-ROC) metrics. Results: After applying the univariate and multivariate analysis, 13 variables were selected as important features in predicting COVID-19 mortality at P < 0.05. A comparison of the two ANN architectures using the MSE showed that the BP-ANN (validation error: 0.067, most of the classified samples having 0.049 and 0.05 error rates, and AUC-ROC: 0.888) was the best model. Conclusions: Our findings show the acceptable performance of ANN for predicting the risk of mortality in hospitalized COVID-19 patients. Application of the developed ANN-based CDSS in a real clinical environment will improve patient safety and reduce disease severity and mortality.

Item Type: Article
Shanbehzadeh, M.UNSPECIFIED
Kazemi-Arpanahi, H.UNSPECIFIED
Keywords: Artificial intelligence Covid-19 Machine learning Neural networks personal relationships that could have appeared to influence the work reported in this paper.
Page Range: p. 100983
Journal or Publication Title: Informatics in medicine unlocked
Journal Index: Pubmed
Volume: 31
Identification Number:
ISSN: 2352-9148 (Print) 2352-9148 (Linking)
Depositing User: مهندس مهدی شریفی

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