Transporting an Artificial Intelligence Model to Predict Emergency Cesarean Delivery: Overcoming Challenges Posed by Interfacility Variation

被引：0

作者：

Guedalia, Joshua ^{[1
]}

Lipschuetz, Michal ^{[1
,2
,3
]}

Cohen, Sarah M. ^{[2
,3
]}

Sompolinsky, Yishai ^{[2
,3
]}

Walfisch, Asnat ^{[2
,3
]}

Sheiner, Eyal ^{[4
]}

Sergienko, Ruslan ^{[5
]}

Rosenbloom, Joshua ^{[2
,3
]}

Unger, Ron ^{[1
]}

Yagel, Simcha ^{[2
,3
]}

Hochler, Hila ^{[2
,3
]}

机构：

[1] Bar Ilan Univ, Mina & Everard Goodman Fac Life Sci, Ramat Gan, Israel

[2] Hebrew Univ Jerusalem, Hadassah Med Org, Div Obstet & Gynecol, Churchill Avn 8, IL-9765415 Jerusalem, Israel

[3] Hebrew Univ Jerusalem, Fac Med, Churchill Avn 8, IL-9765415 Jerusalem, Israel

[4] Ben Gurion Univ Negev, Soroka Univ, Med Ctr, Dept Obstet & Gynecol, Beer Sheva, Israel

[5] Ben Gurion Univ Negev, Fac Hlth Sci, Sch Publ Hlth, Dept Publ Hlth, Beer Sheva, Israel

来源：

JOURNAL OF MEDICAL INTERNET RESEARCH | 2021年 / 23卷 / 12期

关键词：

machine learning; algorithm transport; health outcomes; health care facilities; artificial intelligence; AI; ML; pregnancy; birth; pediatrics; neonatal; prenatal;

D O I：

10.2196/28120

中图分类号：

R19 [保健组织与事业（卫生事业管理）];

学科分类号：

摘要：

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.

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页数：5

共 3 条

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