Interoperable and explainable machine learning models to predict morbidity and mortality in acute neurological injury in the pediatric intensive care unit: secondary analysis of the TOPICC study

Interoperable and explainable machine learning models to predict morbidity and mortality in acute neurological injury in the pediatric intensive care unit: secondary analysis of the TOPICC study

BackgroundAcute neurological injury is a leading cause of permanent disability and death in the pediatric intensive care unit (PICU). No predictive model has been validated for critically ill children with acute neurological injury.ObjectivesWe hypothesized that PICU patients with concern for acute...

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Bibliographic Details
Main Authors: Neil K. Munjal, Robert S. B. Clark, Dennis W. Simon, Patrick M. Kochanek, Christopher M. Horvat
Format: Article
Language:English
Published: Frontiers Media S.A. 2023-06-01
Series:Frontiers in Pediatrics
Subjects:
machine learning
clinical decision support
pediatric intensive care unit
acute neurological injury
predictive modeling
Online Access:https://www.frontiersin.org/articles/10.3389/fped.2023.1177470/full

Internet

https://www.frontiersin.org/articles/10.3389/fped.2023.1177470/full

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