Transporting an Artificial Intelligence Model to Predict Emergency Cesarean Delivery: Overcoming Challenges Posed by Interfacility Variation.
AI
ML
algorithm transport
artificial intelligence
birth
health care facilities
health outcomes
machine learning
neonatal
pediatrics
pregnancy
prenatal
Journal
Journal of medical Internet research
ISSN: 1438-8871
Titre abrégé: J Med Internet Res
Pays: Canada
ID NLM: 100959882
Informations de publication
Date de publication:
10 12 2021
10 12 2021
Historique:
received:
22
02
2021
accepted:
29
10
2021
revised:
25
08
2021
entrez:
10
12
2021
pubmed:
11
12
2021
medline:
21
12
2021
Statut:
epublish
Résumé
Research using artificial intelligence (AI) in medicine is expected to significantly influence the practice of medicine and the delivery of health care in the near future. However, for successful deployment, the results must be transported across health care facilities. We present a cross-facilities application of an AI model that predicts the need for an emergency caesarean during birth. The transported model showed benefit; however, there can be challenges associated with interfacility variation in reporting practices.
Identifiants
pubmed: 34890352
pii: v23i12e28120
doi: 10.2196/28120
pmc: PMC8709908
doi:
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
e28120Informations de copyright
©Joshua Guedalia, Michal Lipschuetz, Sarah M Cohen, Yishai Sompolinsky, Asnat Walfisch, Eyal Sheiner, Ruslan Sergienko, Joshua Rosenbloom, Ron Unger, Simcha Yagel, Hila Hochler. Originally published in the Journal of Medical Internet Research (https://www.jmir.org), 10.12.2021.
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