Use of an artificial intelligence-based rule extraction approach to predict an emergency cesarean section.

Objective: One of the major problems with artificial intelligence (AI) is that it is generally known as a “black box”. Therefore, the present study aimed to construct an emergency cesarean section (CS) prediction system using an AI-based rule extraction approach as a “white box” to detect the cause for the emergency CS. Methods: Data were collected from all perinatal records of all delivery outcomes at Osaka Medical College between December 2014 and July 2019. We identified the delivery method for all deliveries after 36 gestational weeks as either (1) vaginal delivery or scheduled CS, or (2) emergency CS. From among these, we selected 52 risk factors to feed into an AI-based rule extraction algorithm to extract rules to predict an emergency CS. Results: We identified 1513 singleton deliveries (1285 [84.9%] vaginal deliveries, 228 emergency CS [15.1%]) and extracted 15 rules. We achieved an average accuracy of 81.90% using five-fold cross-validation and an area under the receiving operating characteristic curve of 71.46%. Conclusion: To our knowledge, this is the first study to use interpretable AI-based rule extraction technology to predict an emergency CS. This system appears to be useful for identifying hidden factors for emergency CS. [ABSTRACT FROM AUTHOR]