Predicting hospital readmission risk in patients with COVID-19: A machine learning approach.
Inform Med Unlocked
; 30: 100908, 2022.
Article
in English
| MEDLINE | ID: covidwho-1729840
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 andmethods:
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.
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
Full text:
Available
Collection:
International databases
Database:
MEDLINE
Type of study:
Experimental Studies
/
Prognostic study
/
Randomized controlled trials
Language:
English
Journal:
Inform Med Unlocked
Year:
2022
Document Type:
Article
Affiliation country:
J.imu.2022.100908
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