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

Chuin-Hen Liew and Song-Quan Ong and David Chun-Ern Ng (2024) Utilizing machine learning to predict hospital admissions for pediatric COVID-19 patients (PrepCOVID-Machine). Nature Portfolio, 15 (1). pp. 1-13.

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Abstract

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 validate ML models to predict pediatric COVID-19 hospitalization. We collected secondary data with 2200 patients and 65 variables from Malaysian aged 0 to 12 with COVID-19 between 1st February 2020 and 31st March 2022. The sample was partitioned into training, internal, and external validation groups. Recursive Feature Elimination (RFE) was employed for feature selection, and we trained seven supervised classifiers. Grid Search was used to optimize the hyperparameters of each algorithm. The study analyzed 1988 children and 30 study variables after data were processed. The RFE algorithm selected 12 highly predicted variables for COVID-19 hospitalization, including age, male sex, fever, cough, rhinorrhea, shortness of breath, vomiting, diarrhea, seizures, body temperature, chest indrawing, and abnormal breath sounds. With external validation, Adaptive Boosting was the highestperforming classifier (AUROC = 0.95) to predict COVID-19 hospital admission in children. We validated AdaBoost as the best to predict COVID-19 hospitalization among children. This model may assist frontline clinicians in making medical disposition decisions.

Item Type:	Article
Keyword:	Hospital admissions, Pediatric COVID-19, SARS-CoV-2, Artificial intelligence, Machine learning
Subjects:	R Medicine > RA Public aspects of medicine > RA1-1270 Public aspects of medicine > RA421-790.95 Public health. Hygiene. Preventive medicine > RA643-645 Disease (Communicable and noninfectious) and public health R Medicine > RJ Pediatrics > RJ1-570 Pediatrics > RJ370-550 Diseases of children and adolescents > RJ499-507 Mental disorders. Child psychiatry
Department:	INSTITUTE > Institute for Tropical Biology and Conservation
Depositing User:	ABDULLAH BIN SABUDIN -
Date Deposited:	26 May 2025 09:14
Last Modified:	26 May 2025 09:14
URI:	https://eprints.ums.edu.my/id/eprint/43881

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