ANALYSIS OF PRESCRIPTION PATTERNS OF TRADITIONAL CHINESE MEDICINE FORMULAS FOR STROKE TREATMENT
Keywords:
Knowledge graph, Traditional Chinese medicine formulas, Prescription pattern, StrokeAbstract
Stroke is a leading cause of death and disability worldwide. Despite the extensive use of traditional Chinese medicine (TCM) formulas in stroke treatment, systematic analyses of their multi-drug synergy and compatibility patterns remain scarce. This study leverages knowledge graph technology to integrate multi-source TCM formula data and constructs a knowledge graph for stroke-related TCM prescriptions. By combining frequency analysis, cluster analysis, and association rule mining, the study systematically uncovers medication patterns. A total of 1,403 validated formulas were included, identifying nine high-frequency herbs (frequency ≥ 200), such as Saposhnikoviae Radix and Glycyrrhizae Radix. These were categorized into four synergistic clusters. Five strong association rules were identified (e.g., “Ligustici Rhizoma and Almond → Ephedrae Herba” and “Scutellaria Baicalensis and Saposhnikoviae Radix → Glycyrrhizae Radix”). The utility of the knowledge graph in multidimensional retrieval and intelligent reasoning was validated. This study provides data support and a methodological paradigm for the standardized application and modernization of TCM in stroke treatment.References
[1] Parisa Fallahtafti, Amirhossein Habibzadeh, Negar Ghasemloo, et al. Stroke burden in North Africa and the Middle East, 1990-2021: an analysis based on the global burden of disease study. BMC neurology, 2025, 25 (1), 277-277.
[2] Yin Xinyi, Li Shutang, Wang Junwei, et al. Research progress of active compounds from traditional Chinese medicine in the treatment of stroke. European journal of medicinal chemistry, 2025, 291(5), 117599.
[3] Xu Min, Wu RuiXia, Li XiaoLi, et al. Traditional medicine in China for ischemic stroke: bioactive components, pharmacology, and mechanisms. Journal of integrative neuroscience, 2022, 21 (1), 26-26.
[4] Zhao Jianfeng, Zhang Wei, Yu Hai. Prospects and Analysis of Traditional Chinese Medicine Standards Through the Transition of Chinese Pharmacopeia. Journal of Pharmaceutical Innovation, 2022, 18 (2), 349-355.
[5] Zhang Peng, Zhang Dingfan, Zhou Wuai, et al. Network pharmacology: towards the artificial intelligence-based precision traditional Chinese medicine. Briefings in bioinformatics, 2023, 25 (1), 1-12.
[6] Zhang Zheng, Wu Hengyang, Wang Na. A Knowledge-Enhanced Disease Diagnosis Method Based on Prompt Learning and BERT Integration. Journal on Artificial Intelligence, 2025, 7 (1), 17-37.
[7] Gao Yanjun, Li Ruizhe, Emma Croxford, et al. Leveraging Medical Knowledge Graphs Into Large Language Models for Diagnosis Prediction: Design and Application Study. JMIR AI, 2025, 4(1), e58670: p1-p17.
[8] Alain García Olea, Ane G Domingo Aldama, Marcos Merino, et al. The Application of Deep Learning Tools on Medical Reports to Optimize the Input of an Atrial-Fibrillation-Recurrence Predictive Model. Journal of Clinical Medicine, 2025, 14 (7), 2297-2297.
[9] Priyanka Khalate, Shilpa Gite, Biswajeet Pradhan, et al. Advancements and gaps in natural language processing and machine learning applications in healthcare: a comprehensive review of electronic medical records and medical imaging. Frontiers in Physics, 2024, 12(12), 1445204-1445204.