Predicting hospital readmission risk in patients with COVID-19: A machine learning approach.
In: Informatics in medicine unlocked, Jg. 30 (2022), S. 100908
Online
academicJournal
Zugriff:
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.
Competing Interests: The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
(© 2022 Published by Elsevier Ltd.)
Titel: |
Predicting hospital readmission risk in patients with COVID-19: A machine learning approach.
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Autor/in / Beteiligte Person: | Afrash, MR ; Kazemi-Arpanahi, H ; Shanbehzadeh, M ; Nopour, R ; Mirbagheri, E |
Link: | |
Zeitschrift: | Informatics in medicine unlocked, Jg. 30 (2022), S. 100908 |
Veröffentlichung: | [London] : Elsevier Ltd., [2015]-, 2022 |
Medientyp: | academicJournal |
ISSN: | 2352-9148 (print) |
DOI: | 10.1016/j.imu.2022.100908 |
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