A Machine Learning-Based Model for Predicting Therapeutic Responses to Integrated Traditional Chinese Medicine Interventions

Abstract

Integrated Traditional Chinese Medicine (TCM) interventions often involve multiple therapeutic modalities, individualized treatment adjustments, and heterogeneous patient characteristics, making treatment-response prediction difficult using conventional analytical approaches. This study proposes a machine learning-based framework for predicting therapeutic responses to integrated TCM interventions by incorporating patient characteristics, baseline clinical manifestations, treatment-related variables, and longitudinal response indicators. The framework considers the challenges of heterogeneous clinical data, incomplete observations, variable treatment combinations, and potential differences in response patterns among individuals. Several stages of data preprocessing, feature representation, model development, and performance evaluation are considered, with particular attention to the interpretability of predictive outputs and the clinical meaning of selected variables. Rather than assuming that a predictive model can fully capture the complexity of TCM intervention, the proposed approach is intended to provide probabilistic decision-support information that may assist in identifying potentially relevant response patterns and treatment-related factors. Further research is needed to evaluate model robustness, external validity, and clinical usefulness across different populations and intervention settings.