Development of a deep learning model that predicts critical events of pediatric patients admitted to general wards.

ABSTRACT: Early detection of deteriorating patients is important to prevent life-threatening events and improve clinical outcomes. Efforts have been made to detect or prevent major events such as cardiopulmonary resuscitation, but previously developed tools are often complicated and time-consuming, rendering them impractical. To overcome this problem, we designed this study to create a deep learning prediction model that predicts critical events with simplified variables. This retrospective observational study included patients under the age of 18 who were admitted to the general ward of a tertiary children's hospital between 2020 and 2022. A critical event was defined as cardiopulmonary resuscitation, unplanned transfer to the intensive care unit, or mortality. The vital signs measured during hospi

SUBMITTER: Jeon Y 

PROVIDER: S-EPMC10897152 | biostudies-literature | 2024 Feb

REPOSITORIES: biostudies-literature