ArticleJournal of evidence-based medicine2026
AI-Assisted Literature Mining Reveals Spatiotemporal Heterogeneity and Progression Trajectories of Traditional Chinese Medicine Syndromes in Coronary Heart Disease in China.
Article in Journal of evidence-based medicine, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
Despite the centrality of syndrome differentiation in guiding personalized traditional Chinese medicine (TCM) interventions for coronary heart disease (CHD), existing studies of TCM syndrome distribution are constrained by fragmented methodologies and limited spatiotemporal resolution. In this study, we employed an artificial intelligence (AI)-assisted literature mining framework combined with statistical and pattern-mining analyses to characterize the spatiotemporal patterns and evolutionary dynamics of CHD syndrome elements across China. We mined data from 651 peer-reviewed publications reporting 234,656 CHD patients, published between January 2000 and December 2023. A customized constrained Sequential PAttern Discovery using Equivalence classes algorithm was applied to quantify syndrome transition trajectories, while hierarchical clustering integrated with similarity-matrix heatmaps was utilized to detect syndrome co-occurrence patterns. Analyses were stratified by four disease stages, six geographic regions and three periods (2000-2009, 2010-2019, post-2020). Meta-analysis and clustering results revealed that blood stasis, qi deficiency and phlegm emerged as conserved core syndrome elements across all stages, dynamically interacting with qi-yin dual deficiency and phlegm-turbidity as the disease progressed. Geospatial analysis identified a Northern China predominance of qi stagnation-blood stasis, whereas humid subtropical regions exhibited accelerated phlegm-stasis synergy. Temporal analysis uncovered a paradigm shift toward blood stasis-phlegm synergy, coinciding with modern dietary transitions and sedentary lifestyle trends. By leveraging AI-driven methodologies, this study reveals the spatiotemporal heterogeneity of CHD syndrome patterns and underscores the necessity for geographically personalized and temporally adaptive TCM therapeutic strategies for CHD management.
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