A Systematic Review on Machine Learning Algorithms to Predict the Length of Stay for COVID-19 Patients DOI Open Access
Mohammadjavad Sayadi,

Ahmadali Sadeghian Yazdeli,

Hanieh Asaadi Vaskas

et al.

Frontiers in Health Informatics, Journal Year: 2023, Volume and Issue: 12, P. 174 - 174

Published: Dec. 26, 2023

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

Language: Английский

A Systematic Review on Machine Learning Algorithms to Predict the Length of Stay for COVID-19 Patients DOI Open Access
Mohammadjavad Sayadi,

Ahmadali Sadeghian Yazdeli,

Hanieh Asaadi Vaskas

et al.

Frontiers in Health Informatics, Journal Year: 2023, Volume and Issue: 12, P. 174 - 174

Published: Dec. 26, 2023

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

Language: Английский

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