Time-Series Modeling and Intelligent Analysis of Therapeutic Responses to Traditional Chinese Medicine Interventions

Abstract

Traditional Chinese medicine (TCM) interventions often involve therapeutic responses that evolve over time, whereas conventional clinical analyses may treat repeated observations as relatively independent measurements, potentially obscuring temporal dependencies and heterogeneous response patterns. This study proposes a time-series modeling framework for the intelligent analysis of therapeutic responses to TCM interventions, focusing on longitudinal symptom measurements, physiological indicators, treatment-related variables, and time-dependent intervention characteristics. Building on clinical research involving integrated acupuncture, Tuina, and moxibustion, together with theoretical discussions of time-based qi transformation and meridian time sequence, the proposed framework incorporates temporal feature construction, multivariate time-series analysis, and machine-learning techniques to characterize response trajectories and explore possible relationships between intervention timing and clinical outcomes. Particular attention is given to practical challenges, including irregular observation intervals, missing measurements, individual heterogeneity, and the uncertain translation of traditional theoretical concepts into computable variables. Rather than presuming that a single algorithm can adequately capture therapeutic dynamics, several modeling strategies are considered from complementary perspectives. The framework may provide a computational basis for longitudinal evaluation and personalized TCM intervention, although further validation with sufficiently large and heterogeneous clinical datasets is needed.