Repository of Research and Investigative Information

Repository of Research and Investigative Information

Ilam University of Medical Sciences

Predicting hospital readmission risk in patients with COVID-19: A machine learning approach

Thu Jul 25 23:14:21 2024

(2022) Predicting hospital readmission risk in patients with COVID-19: A machine learning approach. Informatics in medicine unlocked. p. 100908. ISSN 2352-9148 (Print) 2352-9148 (Linking)

Full text not available from this repository.

Official URL: https://www.ncbi.nlm.nih.gov/pubmed/35280933

Abstract

Introduction: The Coronavirus 2019 (COVID-19) epidemic stunned the health systems with severe scarcities in hospital resources. In this critical situation, decreasing COVID-19 readmissions could potentially sustain hospital capacity. This study aimed to select the most affecting features of COVID-19 readmission and compare the capability of Machine Learning (ML) algorithms to predict COVID-19 readmission based on the selected features. Material and methods: The data of 5791 hospitalized patients with COVID-19 were retrospectively recruited from a hospital registry system. The LASSO feature selection algorithm was used to select the most important features related to COVID-19 readmission. HistGradientBoosting classifier (HGB), Bagging classifier, Multi-Layered Perceptron (MLP), Support Vector Machine ((SVM) kernel = linear), SVM (kernel = RBF), and Extreme Gradient Boosting (XGBoost) classifiers were used for prediction. We evaluated the performance of ML algorithms with a 10-fold cross-validation method using six performance evaluation metrics. Results: Out of the 42 features, 14 were identified as the most relevant predictors. The XGBoost classifier outperformed the other six ML models with an average accuracy of 91.7, specificity of 91.3, the sensitivity of 91.6, F-measure of 91.8, and AUC of 0.91. Conclusion: The experimental results prove that ML models can satisfactorily predict COVID-19 readmission. Besides considering the risk factors prioritized in this work, categorizing cases with a high risk of reinfection can make the patient triaging procedure and hospital resource utilization more effective.

Item Type: Article
Creators:
CreatorsEmail
Afrash, M. R.UNSPECIFIED
Kazemi-Arpanahi, H.UNSPECIFIED
Shanbehzadeh, M.UNSPECIFIED
Nopour, R.UNSPECIFIED
Mirbagheri, E.UNSPECIFIED
Keywords: AUC, Area under the curve Artificial intelligent CDSS, Clinical Decision Support Systems Covid-19 COVID-19, Coronavirus disease 2019 CRISP, Cross-Industry Standard Process Coronavirus HGB, Hist Gradient Boosting LASSO, Least Absolute Shrinkage and Selection Operator ML, Machine learning MLP, Multi-Layered Perceptron Machine learning Readmission SVM, Support Vector Machine XGBoost, Extreme Gradient Boosting personal relationships that could have appeared to influence the work reported in this paper.
Divisions:
Page Range: p. 100908
Journal or Publication Title: Informatics in medicine unlocked
Journal Index: Pubmed
Volume: 30
Identification Number: https://doi.org/10.1016/j.imu.2022.100908
ISSN: 2352-9148 (Print) 2352-9148 (Linking)
Depositing User: مهندس مهدی شریفی
URI: http://eprints.medilam.ac.ir/id/eprint/4093

Actions (login required)

View Item View Item