Objective  To explore the regularity of prescriptions for epidemic disease in Taiping Huimin Heji Ju Fang based on the latent structure model and association rules analysis, and to provide references for modern epidemic treatment.

Methods  Prescriptions for epidemic diseases were extracted from Taiping Huimin Heji Ju Fang. A high-frequency herb matrix (frequency ≥10) was constructed, and high-frequency herbs were analyzed using Microsoft Excel 2016, Lantern 5.0, and IBM SPSS Modeler 18.0 for efficacy classification, property/flavor/channel tropism statistics, latent structure modeling, and association rule analysis.

Results  Among the 200 collected herbal prescriptions, 46 high-frequency medicinal materials were identified, such as licorice, fresh ginger, dried ginger, poriae, and ginseng. The top 3 drugs efficacy were tonifying deficiency, relieving surface and warming inner. The medicinal properties were mainly warm, and the medicinal flavors were pungent, bitter, and sweet. The meridian tropisms mainly included the spleen meridian, lung meridian and stomach meridian. The analysis of latent structure model suggested that there were 8 types of common symptoms of epidemic disease, such as wind evil attack exterior, wind-cold-dampness, wind-heat, excess-heat in triple energizer, dampness inhibits qi stagnation, yang deficiency, blockage and spleen-qi deficiency. The analysis of association rules obtained licorice-ephedra and licorice-atractylodes, which with a core of licorice medicine, and the 16 association rules such as dried ginger-cinnamon, pericarpium citri reticulatae-mangnolia officinalis and poriae-ginseng-atractylodes macrocephala after eliminating ginger, jujube and licorice.

Conclusion  Most of the medicines used in the treatment of epidemic diseases in Taiping Huimin Heji Ju Fang are pungent, bitter and dispelling evil, the treatment should follow the principle of dispelling evil, warming yang to dissipate cold, dispelling dampness, clearing heat and expelling fire, inducing resuscitation, supporting the right and supplementing deficiency, which embodies the treatment principle of dispelling evil and supporting right, and provides reference and ideas for the treatment of clinical diseases based on syndrome differentiation.

1.刘铁钢, 李晶晶, 董斐, 等. 疫病的辨证与辨病[J]. 中华中医药杂志, 2022, 37(7): 3657-3661. [Liu TG, Li JJ, Dong F, et al. Syndrome differentiation and disease identification in epidemic diseases[J]. China Journal of Traditional Chinese Medicine and Pharmacy, 2022, 37(7): 3657-3661.] https://d.wanfangdata.com.cn/periodical/Ch9QZXJpb2RpY2FsQ0hJTmV3UzIwMjUwMTE2MTYzNjE0Eg96Z3l5eGIyMDIyMDcwMDIaCDdxcG9iYmJm.

2.崔佳旗, 张书与, 韩雅乐, 等. 基于膜系理论探究疫病的防治[J]. 中华中医药杂志, 2023, 38(10): 4557-4561. [Cui JQ, Zhang SY, Han YL, et al. Exploring the prevention and treatment of epidemic diseases based on membrane system theory[J]. China Journal of Traditional Chinese Medicine and Pharmacy, 2023, 38(10): 4557-4561.] https://d.wanfangdata.com.cn/periodical/Ch9QZXJpb2RpY2FsQ0hJTmV3UzIwMjUwMTE2MTYzNjE0Eg96Z3l5eGIyMDIzMTAwMDYaCHNsb2hsd3Rl.

3.Zhang C, Fu X, Liu Y, et al. Burden of infectious diseases and bacterial antimicrobial resistance in China: a systematic analysis for the global burden of disease study 2019[J]. Lancet Reg Health West Pac, 2024, 43: 100972. DOI: 10.1016/j.lanwpc. 2023.100972.

4.Javanian M, Barary M, Ghebrehewet S, et al. A brief review of influenza virus infection[J]. J Med Virol, 2021, 93(8): 4638-4646. DOI: 10.1002/jmv.26990.

5.Gaitonde DY, Moore FC, Morgan MK. Influenza: diagnosis and treatment[J]. Am Fam Physician, 2019, 100(12): 751-758. https://pubmed.ncbi.nlm.nih.gov/31845781/.

6.Wikramaratna PS, Gupta S. Influenza outbreaks[J]. Cell Microbiol, 2009, 11(7): 1016-1024. DOI: 10.1111/j.1462-5822.2009.01320.x.

7.高洁, 肖光旭, 樊官伟, 等. “三药三方”治疗COVID-19的临床和药理研究进展[J]. 科学通报, 2022, 67(27): 3296-3310. [Gao J, Xiao GX, Fan GW, et al. Clinical and pharmacological research progress of "three drugs and three formulas" in the treatment of COVID-19[J]. Chinese Science Bulletin, 2022, 67(27): 3296-3310.] DOI: 10.1360/TB-2022-0375.

8.Liu Z, Li X, Gou C, et al. Effect of Jinhua Qinggan granules on novel coronavirus pneumonia in patients[J]. J Tradit Chin Med, 2020, 40(3): 467-472. DOI: 10.19852/j.cnki.jtcm.2020.03.016.

9.Hu K, Guan WJ, Bi Y, et al. Efficacy and safety of Lianhuaqingwen capsules, a repurposed Chinese herb, in patients with coronavirus disease 2019: a multicenter, prospective, randomized controlled trial[J]. Phytomedicine, 2021, 85: 153242. DOI: 10.1016/j.phymed.2020.153242.

10.Guo H, Zheng J, Huang G, et al. Xuebijing injection in the treatment of COVID-19: a retrospective case-control study[J]. Ann Palliat Med, 2020, 9(5): 3235-3248. DOI: 10.21037/apm-20-1478.

11.Wang Q, Zhu H, Li M, et al. Efficacy and safety of Qingfei Paidu decoction for treating COVID-19: a systematic review and Meta-analysis[J]. Front Pharmacol, 2021, 12: 688857. DOI: 10.3389/fphar.2021.688857.

12.Xiong WZ, Wang G, Du J, et al. Efficacy of herbal medicine (Xuanfei Baidu decoction) combined with conventional drug in treating COVID-19: a pilot randomized clinical trial[J]. Integr Med Res, 2020, 9(3): 100489. DOI: 10.1016/j.imr.2020.100489.

13.吴婷婷, 张怀琼. 中医药古籍中疫病咽喉症状用药规律探析 [J]. 数理医药学杂志, 2023, 36(9): 650-656. [Wu TT, Zhang HQ. Analysis of medication patterns for throat symptoms in epidemic diseases in traditional Chinese medicine ancient texts[J]. Journal of Mathematical Medicine, 2023, 36(9): 650-656.] DOI: 10.12173/j.issn.1004-4337.202304181.

14.王晓鹏, 林孟柯, 梁立新, 等. 《肘后备急方》中疫病用药的数据挖掘研究[J]. 中国中医急症, 2022, 31(11): 1896-1899. [Wang XP, Lin MK, Liang LX, et al. Data mining research on medications for epidemic diseases in Zhouhou Beiji Fang[J]. Journal of Emergency in Traditional Chinese Medicine, 2022, 31(11): 1896-1899.] DOI: 10.3969/j.issn.1004-745X.2022.11.004.

15.张天宇, 吴振起, 任园园, 等. 古代肺系疫病处方用药规律探析[J]. 中国中医药信息杂志, 2021, 28(11): 15-20. [Zhang TY, Wu ZQ, Ren YY, et al. Analysis of medication patterns in ancient prescriptions for pulmonary epidemic diseases[J]. Chinese Journal of Information on Traditional Chinese Medicine, 2021, 28(11): 15-20.] DOI: 10.19879/j.cnki.1005-5304.202105336.

16.白明, 李杨波, 苗明三. 基于古籍数据挖掘的中医防治疫病用药规律分析[J]. 中药药理与临床, 2020, 36(1): 32-36. [Bai M, Li YB, Miao MS. Analysis of medication patterns for prevention and treatment of epidemic diseases in traditional Chinese medicine based on ancient literature data mining[J]. Pharmacology and Clinics of Chinese Materia Medica, 2020, 36(1): 32-36.] DOI: 10.13412/j.cnki.zyyl.20200228.001.

17.刘铁钢, 白辰, 胡莉, 等. 疫病中医病名探究[J]. 中华中医药杂志, 2021, 36(4): 1805-1808. [Liu TG, Bai C, Hu L, et al. Exploration of disease names for epidemics in traditional Chinese medicine[J]. China Journal of Traditional Chinese Medicine and Pharmacy, 2021, 36(4): 1805-1808.] https://d.wanfangdata.com.cn/periodical/Ch9QZXJpb2RpY2FsQ0hJTmV3UzIwMjUwMTE2MTYzNjE0Eg96Z3l5eGIyMDIxMDQwMDUaCDI5Njl2dWlo.

18.韩毅，主编. 宋代瘟疫的流行与防治[M]. 北京: 商务印书馆, 2015: 80-97.

19.国家市场监督管理总局, 国家标准化管理委员会. 中医病证分类与代码（GBT 15657-2021）[S]. 2021.

20.中国药典2020年版. 一部[S]. 2020: 4-402.

21.中华本草编委会. 中华本草[M]. 上海: 上海科学技术出版社, 1999: 207-209.

22.杨柏灿, 钟赣生, 主编. 全国中医药行业高等教育“十四五”规划教材中药学[M]. 北京: 中国中医药出版社, 2021: 56-410.

23.张连文, 许朝霞, 王忆勤, 等. 隐结构分析与西医疾病的辨证分型（Ⅱ）: 综合聚类[J]. 世界科学技术（中医药现代化）, 2012, 14(2): 1422-1427. [Zhang LW, Xu ZX, Wang YQ, et al. Latent structure analysis and syndrome differentiation typing of western medicine diseases(Ⅱ): comprehensive clustering[J]. World Science and Technology-Modernization of Traditional Chinese Medicine, 2012, 14(2): 1422-1427.] DOI: 10.3969/j.issn.1674-3849.2012.02.018.

24.张连文, 袁世宏, 王天芳, 等. 隐结构分析与西医疾病辨证分型（Ⅰ）:基本原理[J]. 世界科学技术（中医药现代化）, 2011, 13(3): 498-502. [Zhang LW, Yuan SH, Wang TF, et al. Latent structure analysis and syndrome differentiation typing of western medicine diseases(Ⅰ): fundamental principles[J]. World Science and Technology-Modernization of Traditional Chinese Medicine, 2011, 13(3): 498-502.] DOI: 10.3969/j.issn. 1674-3849.2011.03.012.

25.许玉龙, 吴秀艳, 李延龙, 等. 基于隐结构分析建立中医证候分型规则的三种方法[J]. 世界科学技术-中医药现代化, 2019, 21(1): 101-108. [Xu YL, Wu XY, Li YL, et al. Three methods for establishing TCM syndrome differentiation rules based on latent structure analysis[J]. World Science and Technology-Modernization of Traditional Chinese Medicine, 2019, 21(1): 101-108.] DOI: 10.11842/wst.2019.01.018.

26.张连文, 傅晨, 刘腾飞, 等. 隐结构分析与西医疾病的辨证分型（Ⅲ）:辨证规则的建立[J]. 世界科学技术-中医药现代化, 2014, 16(4): 723-730. [Zhang LW, Fu C, Liu TF, et al. Latent structure analysis and syndrome differentiation typing of western medicine diseases(Ⅲ): establishment of syndrome differentiation rules[J]. World Science and Technology-Modernization of Traditional Chinese Medicine, 2014, 16(4): 723-730. DOI: 10.11842/wst.2014.04.006.

27.张恒, 张葆青. 基于数据挖掘的中医药临床研究述评[J]. 中华中医药杂志, 2020, 35(11): 5378-5383. [Zhang H, Zhang BQ. A critical review of clinical research in traditional Chinese medicine based on data mining[J]. China Journal of Traditional Chinese Medicine and Pharmacy, 2020, 35(11): 5378-5383.]https://d.wanfangdata.com.cn/periodical/Ch9QZXJpb2RpY2FsQ0hJTmV3UzIwMjUwMTE2MTYzNjE0Eg96Z3l5eGIyMDIwMTEwMTAaCG45d3BlcGR0.

28.李浩, 侯贺祥, 倪健, 等. 中医关联规则分析平台存在的缺陷及完善方法[J]. 中华中医药杂志, 2022, 37(11): 6671-6674. [Li H, Hou HX, Ni J, et al. Defects and improvement methods of traditional Chinese medicine association rule analysis platform[J]. China Journal of Traditional Chinese Medicine and Pharmacy, 2022, 37(11): 6671-6674.] https://d.wanfangdata.com.cn/periodical/Ch9QZXJpb2RpY2FsQ0hJTmV3UzIwMjUwMTE2MTYzNjE0Eg96Z3l5eGIyMDIyMTExMTEaCDd1cTdxazJr.

29.马捷, 王馨梓, 何美慧. 基于关联规则的中医药知识发现——以肺病医案为例[J]. 现代情报, 2024, 44(3): 34-46. [Ma J, Wang XZ, He MH. Knowledge discovery in traditional Chinese medicine based on association rules: a case study of pulmonary disease medical records[J]. Journal of Modern Information, 2024, 44(3): 34-46.] https://d.wanfangdata.com.cn/periodical/Ch9QZXJpb2RpY2FsQ0hJTmV3UzIwMjUwMTE2MTYzNjE0EiNxa19mYTQzZGNmZTkxMjQ0MjI1OTFkZDM3YjkyMTJjYzk3MRoIY3B0cmU0cW8%3D.

30.张庆, 姬文帅, 孔欣欣, 等. 基于关联规则和隐结构模型的《普济方》中治疗喘证方剂的用药规律分析[J]. 中草药, 2023, 54(5): 1517-1525. [Zhang Q, Ji WS, Kong XX, et al. Analysis of medication patterns in prescriptions for asthma treatment from Puji Fang based on association rules and latent structure models[J]. Chinese Traditional and Herbal Drugs, 2023, 54(5): 1517-1525.] DOI: 10.7501/j.issn.0253-2670.2023.05.019.

31.杨漾, 苏畅, 程丽媛, 等. 基于隐结构结合关联规则探讨中医古籍中胸痹辨证分型及用药规律[J]. 中药新药与临床药理, 2023, 34(4): 556-563. [Yang Y, Su C, Cheng LY, et al. Exploring syndrome differentiation and medication patterns for chest obstruction in ancient TCM literature based on latent structure and association rules[J]. Traditional Chinese Drug Research and Clinical Pharmacology, 2023, 34(4): 556-563.] DOI: 10.19378/j.issn.1003-9783.2023.04.017.

32.李贞, 黄夏瑾, 殷文静, 等. 基于数据挖掘中药复方专利治疗流行性感冒的用药规律[J]. 药物流行病学杂志, 2024, 33(7): 731-742. [Li Z, Huang XJ, Yin WJ, et al. Medication patterns of TCM compound patents for influenza treatment based on data mining[J]. Chinese Journal of Pharmacoepidemiology, 2024, 33(7): 731-742.] DOI: 10.12173/j.issn.1005-0698.202404043.

33.程海波, 周仲瑛. 周仲瑛“疫毒”学术思想探析[J]. 中医杂志, 2021, 62(7): 564-567.[ Cheng HB, Zhou ZY. Analysis of Zhou Zhongying's academic thoughts on "pestilential toxin"[J]. Journal of Traditional Chinese Medicine, 2021, 62(7): 564-567.] DOI: 10.13288/j.11-2166/r.2021.07.003.

34.王赛, 白明, 苗明三. 基于数据挖掘的《瘟疫论》《广瘟疫论》用药规律分析[J]. 中药药理与临床, 2020, 36(2): 28-32. [Wang S, Bai M, Miao MS. Analysis of medication patterns in Treatise on Pestilence and Expanded Treatise on Pestilence based on data mining[J]. Pharmacology and Clinics of Chinese Materia Medica, 2020, 36(2): 28-32.] DOI: 10.13412/j.cnki.zyyl.20200303.001.

35.李泽宇, 郝二伟, 曹瑞, 等. 基于中医寒疫理论的中药防治疫病用药规律与作用机制分析[J]. 中国中药杂志, 2022, 47(17): 4765-4777. [Li ZY, Hao EW, Cao R, et al. Analysis of medication patterns and mechanism of traditional Chinese medicine for epidemic prevention and treatment based on cold-pestilence theory[J]. China Journal of Chinese Materia Medica, 2022, 47(17): 4765-4777.] DOI: 10.19540/j.cnki.cjcmm.20220528.501.

36.宋佳, 王至婉. 基于不同数据类型的隐结构模型在中医研究中的应用与进展[J]. 世界科学技术-中医药现代化, 2022, 24(5): 2079-2084. [Song J, Wang ZW. Application and progress of latent structure models based on different data types in traditional Chinese medicine Research[J]. World Science and Technology-Modernization of Traditional Chinese Medicine, 2022, 24(5): 2079-2084.] DOI: 10.11842/wst.20210808008.

37.郭雯, 冯贞贞, 王露, 等. 基于文献的肺癌常见中医证候研究[J]. 中医杂志, 2021, 62(16): 1447-1454. [Guo W, Feng ZZ, Wang L, et al. Literature-based study on common TCM syndromes of lung cancer[J]. Journal of Traditional Chinese Medicine, 2021, 62(16): 1447-1454.] DOI: 10.13288/j.11-2166/r.2021.16.014.

38.张东, 冯贞贞, 王露, 等. 基于隐结构结合关联规则的中晚期肺癌方药规律分析[J]. 中国中药杂志, 2022, 47(1): 235-243. [Zhang D, Feng ZZ, Wang L, et al. Analysis of prescription patterns for middle-late stage lung cancer based on latent structure model and association rules[J]. China Journal of Chinese Materia Medica, 2022, 47(1): 235-243.] DOI: 10.19540/j.cnki.cjcmm.20210722.501.

39.雷国奇. 四逆汤类方证治规律及临床应用研究[D]. 武汉: 湖北中医药大学, 2010.

40.李玉丽, 蒋屏, 袁振仪, 等. 经典名方升麻葛根汤关键信息考证[J]. 中国实验方剂学杂志, 2025, 31(8): 187-197. [Li YL, Jiang P, Yuan ZY, et al. Textual research on key information of classical famous formula Shengma Gegen Tang (cimicifuga and pueraria decoction)[J]. Chinese Journal of Experimental Traditional Medical Formulae, 2025, 31(8): 187-197.] DOI: 10.13422/j.cnki.syfjx.20240316.

41.张薇, 熊珮宇, 刘俊宇, 等. 经典名方人参败毒散的历史沿革与现代临床应用[J]. 中国实验方剂学杂志, 2023, 29(19): 60-71. [Zhang W, Xiong PY, Liu JY, et al. Historical evolution and modern clinical application of classical famous formula Renshen Baidu San (Ginseng Detoxification powder)[J]. Chinese Journal of Experimental Traditional Medical Formulae, 2023, 29(19): 60-71.] DOI: 10.13422/j.cnki.syfjx.20230443.

42.张楚楚, 张华敏, 李宗友, 等. 基于温病古籍的寒疫辨治规律分析[J]. 世界科学技术-中医药现代化, 2021, 23(7): 2473-2479. [Zhang CC, Zhang HM, Li ZY, et al. Analysis of diagnosis and treatment patterns for cold pestilence based on warm disease ancient texts[J]. World Science and Technology-Modernization of Traditional Chinese Medicine, 2021, 23(7): 2473-2479.] DOI: 10.11842/wst.20200924004.

43.鞠康, 张盛, 胡云飞, 等. 基于数据挖掘“疫”病方剂用药规律对新型冠状病毒肺炎防治的用药启发[J]. 中药药理与临床, 2021, 37(1): 9-16. [Ju K, Zhang S, Hu YF, et al. Medication insights for COVID-19 prevention and treatment derived from data mining analysis of prescription patterns in epidemic-treating formularies[J]. Pharmacology and Clinics of Chinese Materia Medica, 2021, 37(1): 9-16.] DOI: 10.13412/j.cnki.zyyl.2021. 01.001.