Abstract
We previously developed a machine learning model for selecting an individualized gonadotropin dose for ovarian stimulation based on patient similarity matching [1]. The purpose of this study was to evaluate this methodology on a large and diverse dataset to retrospectively determine associations between machine learning predictions, patient outcomes, and total gonadotropin used.
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