Development of a machine learning model for predicting pediatric mortality in the early stages of intensive care unit admission

Autor: Bongjin Lee, Kyunghoon Kim, Hyejin Hwang, You Sun Kim, Eun Hee Chung, Jong-Seo Yoon, Hwa Jin Cho, June Dong Park
Jazyk: angličtina
Rok vydání: 2021
Předmět:
Zdroj: Scientific Reports, Vol 11, Iss 1, Pp 1-7 (2021)
Druh dokumentu: article
ISSN: 2045-2322
DOI: 10.1038/s41598-020-80474-z
Popis: Abstract The aim of this study was to develop a predictive model of pediatric mortality in the early stages of intensive care unit (ICU) admission using machine learning. Patients less than 18 years old who were admitted to ICUs at four tertiary referral hospitals were enrolled. Three hospitals were designated as the derivation cohort for machine learning model development and internal validation, and the other hospital was designated as the validation cohort for external validation. We developed a random forest (RF) model that predicts pediatric mortality within 72 h of ICU admission, evaluated its performance, and compared it with the Pediatric Index of Mortality 3 (PIM 3). The area under the receiver operating characteristic curve (AUROC) of RF model was 0.942 (95% confidence interval [CI] = 0.912–0.972) in the derivation cohort and 0.906 (95% CI = 0.900–0.912) in the validation cohort. In contrast, the AUROC of PIM 3 was 0.892 (95% CI = 0.878–0.906) in the derivation cohort and 0.845 (95% CI = 0.817–0.873) in the validation cohort. The RF model in our study showed improved predictive performance in terms of both internal and external validation and was superior even when compared to PIM 3.
Databáze: Directory of Open Access Journals
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