This study proposes an interpretable machine learning framework to model bidirectional dynamic interactions between macroscopic norms and microscopic features in clinical data. Leveraging real-world medical records from a specialized chest hospital (containing unstructured text, complex categorical variables, temporal indicators, and non-random missing patterns), we perform numerical processing through Latent Semantic Analysis and dimensionality reduction via Non-negative Matrix Factorization. Macroscopic therapeutic norms are identified using HDBSCAN clustering, while SHAP-XGBoost integration selects critical microscopic features, including multidrug-resistant tuberculosis diagnosis and liver function biomarkers. We integrate symbolic regression with the Peter-Clark Momentary Conditional Independence causal discovery method based on partial correlation, constructing cross-scale functional relationships with temporally rigorous constraints. Specifically, PySR derives nonlinear mapping equations, while partial correlation-based conditional independence tests establish time-lagged dynamic dependency networks. Guided by the Dynamic Maximum Entropy across Scales (DyMES) principle, multi-scale perturbation experiments reveal bidirectional mechanisms. Within our dataset and framework, DyMES reveals dynamic constraints’ interplay driving statistical equilibrium between macroscopic clinical norms and microscopic patient characteristics through nonlinear coordination and threshold-triggered time-encoded mechanisms. Persistent constraint interactions induce novel steady states formation with dynamically preserved system memory.