Data Mining of Classical Formulae for TCM Standardization: Knowledge Discovery, Decision Support, and Validation

Abstract

This paper examines how data-mining studies of classical Chinese medical formulae can inform the standardization of traditional Chinese medicine decision support without reducing clinical reasoning to fixed rules. Focusing on the Menghe medical school’s prescribing patterns for warm diseases, damp-heat warm diseases, and insomnia, the discussion considers frequency analysis, association rule mining, and cluster analysis as hypothesis-generating tools rather than confirmatory evidence. The paper then links these pattern-level findings to preclinical studies on cognitive impairment, including naofuming dripping pills, strain comparisons in Alzheimer’s disease models, Semaphorin 3A expression in focal cerebral ischemia, comparative Chinese herbal compound formulas, and maca extract in scopolamine-induced memory impairment. To some extent, data mining can clarify the internal logic of historical formulae, but source selection, coding decisions, and the absence of clinical outcome data limit its interpretive reach. Cross-industry experiences in digital archives, risk screening, data integration, platform operations, financial governance, and digital communication further suggest that standardization requires interoperable infrastructures and transparent governance. Considering the above factors, this paper proposes a three-stage framework of knowledge discovery, decision support, and experimental validation, while acknowledging that further research is needed to test whether such a framework improves clinical reasoning or merely formalizes existing biases.