A Knowledge Graph-Driven Hypergeometric Efficacy Prediction Model for Classical Traditional Chinese Herbal Formulas

The multilevel semantic structure of traditional Chinese medicine (TCM) formulas makes their efficacy difficult to represent computationally.

This study aimed to develop an interpretable, statistically rigorous model for quantitatively predicting the dominant efficacies of classical TCM herbal formulas.

A knowledge graph encompassing five semantic entities-disease, syndrome, symptom, efficacy, and herb-was constructed to standardize and infer multilevel efficacy relationships. Based on this structure, the Hypergeometric Efficacy Prediction Model (HEPM) was established, using hypergeometric enrichment analysis to assess whether specific efficacies are significantly aggregated within a formula. A curated dataset of 174 classical formulas from authoritative TCM sources was used for model validation.

HEPM effectively reproduced characteristic efficacy patterns of classical prescriptions, achieving an average F1 score of 0.63 across 174 formulas. The knowledge graph structure resolved semantic inconsistency and incompleteness in traditional efficacy descriptions, enhancing the integrity and computability of efficacy information.

HEPM provides a statistically grounded and interpretable framework for modeling efficacy formation in TCM herbal formulas. The method offers a replicable approach for efficacy prediction and supports the development of knowledge-driven intelligent TCM analysis and clinical decision support applications.