additive value of computed tomography severity scores to predict lengths of stay in hospital and icu for covid-19 patients: a machine learning study

Introduction: during the outbreak of covid-19, most hospitals faced resource shortages due to the great surges in the influx of infected covid-19 patients and demand exceeding capacities. predicting the lengths of stay (los) of the patients can help to make proper resource-planning decisions. ct-ss accurately determines the disease severity and could be considered an appropriate prognostic factor to predict patients’ los.in this study, we evaluate the additive value of ct-ss in the prediction of hospital and icu loss of covid-19 patients.methods: this single-center study retrospectively reviewed a hospital-based covid-19 registry database from 6854 cases of suspected covid-19. four well-known ml classification models including knn, mlp, svm, and c4.5 decision tree algorithms were used to predict hospital and icu loss of covid-19 patients. the confusion matrix-based performance measures were used to evaluate the classification performances of the ml algorithms.results: for predicting hospital los, the mlp model with an accuracy of 96.7%, sensitivity of 100.0%, precision of 93.8%, specificity of 93.4%, and auc of around 99.4% had the best performance among the other three ml techniques. this algorithm with 95.3% sensitivity, 86.2% specificity, 90.8% accuracy, 87.3% precision, 91.2% f-measure, and an auc of 95.8% had also the best performance for predicting icu los of the patients.conclusion: the performances of the ml predictive models for predicting hospital and icu loss of covid-19 patients were improved when ct-ss data was integrated into the input dataset.