Enhancing Severe Neutropenia Prediction: PKPD-Informed Labeling for Machine Learning Models Trained on Real-World Data.

Accurately labeling outcomes in real-world data for machine learning is challenging due to data sparsity and imbalances. This study developed and evaluated a pharmacokinetic-pharmacodynamic (PKPD)-informed labeling strategy to enhance the risk prediction of docetaxel-induced neutropenia. Machine learning models were trained on real-world data from 4,248 patients using two approaches for comparison. The "naive" labeling method used only neutrophil observations, while the "PKPD-informed" method used simulations from a semi-mechanistic model to determine the neutrophil nadir for each treatment cycle. Three machine learning models (logistic regression, XGBoost, TabPFN) were trained with baseline laboratory data to predict severe neutropenia (neutrophil count <0.1 cells × 109/L) prior to the first docetaxel dose. The PKPD labeling approach enabled the labeling of 3.4 times more patient instances (7,719 vs. 2,283) than the naive method. Across all machine learning architectures, models trained with PKPD-informed labels demonstrated significantly superior predictive performance (AUC-ROC and AUC-PR) compared to those trained with naive labels. This advantage was maintained even when training set sizes were matched. PKPD-informed labeling overcomes limitations of sparse real-world data, increasing both the quantity and apparent quality of labels for machine learning model training. This methodology enhances the performance of machine learning models for predicting severe neutropenia and represents a robust, generalizable framework for improving clinical outcome prediction.