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• W3200492562 abstract "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 and recursive feature elimination to predict the number of eggs and mature (MII) eggs retrieved using baseline parameters available prior to start of treatment. The predictor variables were then used to create a patient similarity model based on K nearest neighbors (KNN), an interpretable machine learning technique. After identifying the best performing distance metrics, neighbor weights, and number of neighbors, the model was used to predict the number of eggs and MII eggs retrieved by calculating the weighted average from the set of K neighbors most similar to the patient of interest. The performance of the KNN model was compared to linear regression in terms of R-squared (R2) and mean absolute error (MAE). The KNN model was then used to (a) query the K most similar patients, and (b) identify the optimal gonadotropin dose in terms of highest number of MII eggs retrieved. We developed linear regression and KNN models using patient age, BMI, diagnosis, AMH, AFC, number of previous IVF cycles, and parity. KNN achieved highest performance using the Manhattan distance, 50-80 similar patients, and distance-based neighbor weighting. The KNN model outperformed linear regression for eggs retrieved (R2: 0.43 vs. 0.39, MAE: 4.84 vs. 4.98) and for MII eggs retrieved (R2: 0.39 vs. 0.35, MAE: 4.01 vs. 4.11). We then investigated the application of these models for gonadotropin dose selection. Linear models indicated that gonadotropin dose is negatively correlated with MII eggs, which may in part reflect that poor-prognosis patients are prescribed higher doses. In contrast, the KNN model showed that 22% of patients had a concave dose response curve, in which there was an optimal dose that maximized the number of MII eggs. We developed a patient similarity model using K nearest neighbors. The model showed better accuracy than linear regression for predicting eggs and MII eggs retrieved, and allowed the evaluation of which starting dose maximized the number of MII eggs retrieved, which is not possible with a linear model. Future work will optimize techniques for matching similar patients and extend the modeling for protocol selection." @default.
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• W3200492562 date "2021-09-01" @default.
• W3200492562 modified "2023-10-07" @default.
• W3200492562 title "AN INTERPRETABLE MACHINE LEARNING MODEL FOR INDIVIDUALIZED PROTOCOL SELECTION AND GONADOTROPIN DOSE SELECTION DURING OVARIAN STIMULATION" @default.
• W3200492562 doi "https://doi.org/10.1016/j.fertnstert.2021.07.479" @default.
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