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Transporting an AI model to predict emergency cesarean delivery: Overcoming challenges posed by inter-facility feature variation
Joshua Guedalia;
Michal Lipschuetz;
Sarah M. Cohen;
Yishai Sompolinsky;
Asnat Walfisch;
Eyal Sheiner;
Ruslan Sergienko;
Joshua Rosenbloom;
Ron Unger;
Simcha Yagel;
Hila Hochler
ABSTRACT
Research using artificial intelligence in medicine is expected to significantly influence the practice of medicine and the delivery of healthcare 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 inter-facility variation in reporting practices.
Citation
Please cite as:
Guedalia J, Lipschuetz M, Cohen SM, Sompolinsky Y, Walfisch A, Sheiner E, Sergienko R, Rosenbloom J, Unger R, Yagel S, Hochler H
Transporting an Artificial Intelligence Model to Predict Emergency Cesarean Delivery: Overcoming Challenges Posed by Interfacility Variation