An interpretable machine learning model for individualized protocol selection and gonadotropin dose selection during ovarian stimulation

Objective: To develop an interpretable machine learning model for individualized gonadotropin dose selection during controlled ovarian stimulation.

Impact Statement: We developed an interpretable machine learning model for individualized gonadotropin dose selection, which may help to optimize the number of mature eggs retrieved during ovarian stimulation.