Utilizing machine learning to predict hospital admissions for pediatric COVID-19 patients (PrepCOVID-Machine)

• Published in: Scientific reports Vol. 15; no. 1; pp. 3131 - 13
• Main Authors: Liew, Chuin-Hen, Ong, Song-Quan, Ng, David Chun-Ern
• Format: Journal Article
• Language: English
• Published: London Nature Publishing Group UK 24.01.2025; Nature Publishing Group; Nature Portfolio
• Subjects: 692/308/3187; 692/699/255/2514; 692/700/1720/3187; Algorithms; Artificial intelligence; Body temperature; Child; Child, Preschool; Children; Cough; COVID-19; COVID-19 - diagnosis; COVID-19 - epidemiology; Diarrhea; Dyspnea; Female; Hospital admissions; Hospitalization; Hospitalization - statistics & numerical data; Humanities and Social Sciences; Humans; Infant; Infant, Newborn; Learning algorithms; Machine Learning; Malaysia - epidemiology; Male; multidisciplinary; Pandemics; Patients; Pediatric COVID-19; Pediatrics; SARS-CoV-2; SARS-CoV-2 - isolation & purification; Science; Science (multidisciplinary); Seizures; Malaysia; Hospital admissions; SARS-CoV-2; Pediatric COVID-19; Artificial intelligence; Machine learning

The COVID-19 pandemic has burdened healthcare systems globally. To curb high hospital admission rates, only patients with genuine medical needs are admitted. However, machine learning (ML) models to predict COVID-19 hospitalization in Asian children are lacking. This study aimed to develop and valid...