A Systematic Review on Machine Learning Algorithms to Predict the Length of Stay for COVID-19 Patients

Abstract

Introduction: Managing resources is one of the most important challenges that healthcare providers worldwide face during the COVID-19 pandemic. In recent years, machine learning has been developed to provide valuable help in predicting disease and estimating the duration of their stay. This study aimed to identify the machine learning models for predicting length of stay in COVID-19. Material and Methods: Online databases, including Scopus, PubMed, Web of Science, and Science Direct, were searched, and a hand search through Google Scholar and grey literature was done up to August 2023 and updated in December 2023 to identify articles to find all relevant studies. To manage the process and check the quality of included articles PRISMA guidelines and CASP checklist were used and data was extracted using a data extraction form. Results: Among all 489 research articles, 10 studies met the inclusion criteria. The best models reported in the included articles were random forest (n=3), gradient boosting (n=2), XGBoost (n=2), SVM (n=1), KNN (n=1), and DataRobot (n=1). Except one of the studies that used quantitative modeling and reported MSE and MAE as evaluation criteria, other studies used qualitative modeling and reported accuracy, specificity, and F1-score. The focus of the included articles was on the general and ICU departments as the important resources in the hospital and emphasized the use of machine learning to predict the length of stay. Conclusion: The results of this systematic review showed that a data mining approach and using a machine learning algorithm can help to manage the critical resources of the hospital especially when we are faced with a pandemic disease like COVID-19.

Keywords

• machine learning
• length of stay
• covid-19