predicting icu admission for hospitalized covid-19 patients by artificial neural network combined with elastic net

Background: machine learning models could assist physicians in identifying high-risk covid-19 patients. this study aimed to predict the intensive care unit (icu) admission in covid-19 hospitalized patients by the artificial neural network (ann) model combined with elastic net algorithm. methods: in this prospective study, the data of 139 covid-19 patients admitted to imam reza hospital in tabriz between 20 march and 5 april 2020, were analyzed. the elastic net method was used to choose features with high importance. the chosen variables were standardized and ann was fitted to the data with one hidden layer based on the descending gradient algorithm. to validate the model, the training and test group method with a ratio of 70 to 30 was used. the model's predictive power was reported by calculating the overall accuracy, sensitivity, specificity, and area under the roc curve (auc). results: according to the results of the elastic net, the ann model was constructed based on age, sex, body mass index, diabetes mellitus status, history of heart disease, and vital signs including systolic and diastolic blood pressure, saturated oxygen level, pulse rate, respiration rate, and body temperature. the overall accuracy of this model was 93.15%, sensitivity 80%, specificity 95.8%, and auc 0.90. saturated oxygen level, pulse rate, and age were the most important and predictive variables. conclusion: in the investigated sample of patients admitted with covid-19, the fitted ann model had acceptable performance to predict the icu admission. this finding could be useful for physicians and policy makers.