Ignorance Isn't Bliss: We Must Close the Machine Learning Knowledge Gap in Pediatric Critical Care.
artificial intelligence
learning curricula
machine learning
medical education
pediatric critical care medicine
Journal
Frontiers in pediatrics
ISSN: 2296-2360
Titre abrégé: Front Pediatr
Pays: Switzerland
ID NLM: 101615492
Informations de publication
Date de publication:
2022
2022
Historique:
received:
28
01
2022
accepted:
18
04
2022
entrez:
27
5
2022
pubmed:
28
5
2022
medline:
28
5
2022
Statut:
epublish
Résumé
Pediatric intensivists are bombarded with more patient data than ever before. Integration and interpretation of data from patient monitors and the electronic health record (EHR) can be cognitively expensive in a manner that results in delayed or suboptimal medical decision making and patient harm. Machine learning (ML) can be used to facilitate insights from healthcare data and has been successfully applied to pediatric critical care data with that intent. However, many pediatric critical care medicine (PCCM) trainees and clinicians lack an understanding of foundational ML principles. This presents a major problem for the field. We outline the reasons why in this perspective and provide a roadmap for competency-based ML education for PCCM trainees and other stakeholders.
Identifiants
pubmed: 35620143
doi: 10.3389/fped.2022.864755
pmc: PMC9127438
doi:
Types de publication
Journal Article
Langues
eng
Pagination
864755Informations de copyright
Copyright © 2022 Ehrmann, Harish, Morgado, Rosella, Johnson, Mema and Mazwi.
Déclaration de conflit d'intérêts
The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
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