AN INTERPRETABLE MACHINE LEARNING MODEL FOR INDIVIDUALIZED PROTOCOL SELECTION AND GONADOTROPIN DOSE SELECTION DURING OVARIAN STIMULATION

To develop an interpretable machine learning model for individualized gonadotropin dose selection during controlled ovarian stimulation. Historical, de-identified electronic medical record (EMR) data was collected from 4 IVF clinics in the United States. Records were filtered for autologous, non-canceled IVF retrievals, resulting in 7,977 cycles started between 2014 and 2020. A multiple linear regression model was developed with cross validation...