Dies ist eine Übersichtsseite mit Metadaten zu dieser wissenschaftlichen Arbeit. Der vollständige Artikel ist beim Verlag verfügbar.
Transporting an Artificial Intelligence Model to Predict Emergency Cesarean Delivery: Overcoming Challenges Posed by Interfacility Variation
12
Zitationen
11
Autoren
2021
Jahr
Abstract
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.
Ähnliche Arbeiten
A new method of classifying prognostic comorbidity in longitudinal studies: Development and validation
1987 · 49.078 Zit.
Global, regional, and national incidence, prevalence, and years lived with disability for 354 diseases and injuries for 195 countries and territories, 1990–2017: a systematic analysis for the Global Burden of Disease Study 2017
2018 · 13.760 Zit.
Global, regional, and national incidence, prevalence, and years lived with disability for 328 diseases and injuries for 195 countries, 1990–2016: a systematic analysis for the Global Burden of Disease Study 2016
2017 · 13.408 Zit.
The injury severity score: a method for describing patients with multiple injuries and evaluating emergency care.
1974 · 8.023 Zit.
Global, regional, and national incidence, prevalence, and years lived with disability for 310 diseases and injuries, 1990–2015: a systematic analysis for the Global Burden of Disease Study 2015
2016 · 7.311 Zit.