Predicting hospital readmission risk in patients with COVID-19: A machine learning approach.

Afrash, Mohammad Reza; Kazemi-Arpanahi, Hadi; Shanbehzadeh, Mostafa; Nopour, Raoof; Mirbagheri, Esmat

Afrash, Mohammad Reza; Kazemi-Arpanahi, Hadi; Shanbehzadeh, Mostafa; Nopour, Raoof; Mirbagheri, Esmat.

Affiliation

Afrash MR; Department of Health Information Technology and Management, School of Allied Medical Sciences, Shahid Beheshti University of Medical Sciences, Tehran, Iran.
Kazemi-Arpanahi H; Department of Health Information Technology, Abadan Faculty of Medical Sciences, Abadan, Iran.
Shanbehzadeh M; Student Research Committee, Abadan Faculty of Medical Sciences, Abadan, Iran.
Nopour R; Department of Health Information Technology, School of Paramedical, Ilam University of Medical Sciences, Ilam, Iran.
Mirbagheri E; Department of Health Information Management, Student Research Committee, School of Health Management and Information Sciences Branch, Iran University of Medical Sciences, Tehran, Iran.

Inform Med Unlocked ; 30: 100908, 2022.

Article in En | MEDLINE | ID: mdl-35280933

Key words

AUC, Area under the curve; Artificial intelligent; CDSS, Clinical Decision Support Systems; COVID-19; COVID-19, Coronavirus disease 2019; CRISP, Cross-Industry Standard Process; Coronavirus; HGB, Hist Gradient Boosting; LASSO, Least Absolute Shrinkage and Selection Operator; ML, Machine learning; MLP, Multi-Layered Perceptron; Machine learning; Readmission; SVM, Support Vector Machine; XGBoost, Extreme Gradient Boosting

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Full text: 1 Collection: 01-internacional Database: MEDLINE Type of study: Etiology_studies / Prognostic_studies / Risk_factors_studies Language: En Journal: Inform Med Unlocked Year: 2022 Document type: Article Affiliation country: Irán Country of publication: Reino Unido

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