An interpretable machine learning model for predicting the optimal day of trigger during ovarian stimulation

Objective: To develop an interpretable machine learning model for optimizing the day of trigger in terms of mature oocytes (MII), fertilized oocytes (2PNs), and usable blastocysts.

Design: Retrospective study.

Setting: A group of three assisted reproductive technology centers in the United States.

Patient(s): Patients undergoing autologous in vitro fertilization cycles from 2014 to 2020 (n = 30,278).