Proposing an Intelligent Monitoring System for Early Prediction of Need for Intubation among COVID-۱۹ Hospitalized Patients

Introduction: Predicting acute respiratory insufficiency due to coronavirus disease ۲۰۱۹ (COVID-۱۹) can diminish the severe complications and mortality associated with the disease. This study aimed to develop an intelligent system based on machine learning (ML) models for frontline clinicians to effectively triage high-risk patients and prioritize who needs mechanical intubation (MI). Materials and Methods: In this retrospective-design study, the data regarding ۴۸۲ COVID-۱۹ hospitalized patients from February ۹, ۲۰۲۰, to July ۲۰, ۲۰۲۱, was analyzed by six ML classifiers. The most critical clinical variables were identified by a minimal-redundancy-maximal-relevance (mRMR) feature selection technique. In the next step, the models' performance was assessed using confusion matrix criteria and, finally, the best model was adopted. Results: Proposed models were implemented using ۲۳ confirmed variables. Results of comparing six selected ML algorithms indicated the extreme gradient boosting (XGBoost) classifier with ۸۴.۷% accuracy, ۷۶.۵ % specificity, ۹۰.۷% sensitivity, ۸۵.۱% f-measure, ۸۷.۴% Kappa statistic, and ۸۵.۳% for receiver operating characteristic (ROC) had the best performance in the intubation prediction. Conclusion: It is found that ML enables a satisfactory accuracy level in calculating intubation risk in COVID-۱۹ patients. Therefore, using the ML-based intelligent models, notably the XGBoost algorithm, actually enables recognizing high-risk cases and advising correct therapeutic and supportive care by the clinicians.

Keywords:

COVID-۱۹ , Coronavirus , Artificial Intelligence , Machine Learning , Intubation , Prognosis.