An overview of the application of association rule mining (ARM) in real-world study (RWD) on drug safety to inform pharmacovigilance real-world data analysis. The applications of ARM in RWD of drug safety were divided into single drug/vaccine signal detection, combined medication risk mining, multidimensional risk factor analysis and adverse drug event occurrence characterization based on passive monitoring data; medication characterization pattern analysis, auxiliary epidemiological study design and risk mining of the whole dataset based on active monitoring data. In general, foreign scholars pay more attention to method rule setting, performance evaluation and application research, while domestic scholars pay more attention to multidimensional risk factor analysis, adverse drug event occurrence pattern, and clinical drug use characteristics research. With the accumulation of medical data and the continuous development of data mining technology, ARM may provide new ideas for RWD on drug safety.

1.国家药品监督管理局. 国家药监局关于发布真实世界证据支持药物研发与审评的指导原则（试行）的通告（2020年第1号）[EB/OL]. (2020-01-07) [2023-05-03]. https://www.nmpa.gov.cn/yaopin/ypggtg/ypqtgg/20200107151901190.html.

2.Harpaz R, Dumouchel W, Shah NH, et al. Novel data-mining methodologies for adverse drug event discovery and analysis[J]. Clin Pharmacol Ther, 2012, 91(6): 1010-1021. DOI: 10.1038/clpt.2012.50.

3.王蔷, 吉永卓成, 窦丽霞. 真实世界数据在药物警戒工作中的应用[J]. 中国食品药品监管, 2023(10): 60-67. [Wang Q, Takunari Y, Dou LX. Application of real world data in pharmacovigilance[J]. China Food & Grug Administration, 2023(10): 60-67.] DOI: 10.3969/j.issn.1673-5390.2023.10.007.

4.徐圣, 王锴, 廖珊妹. 利用真实世界数据的上市后药品安全监测的统计学方法综述[J]. 中国食品药品监管, 2021(11): 71-82. [Xu S, Wang K, Liao SM. From real-world data to safety monitoring: a overview of statistical methods in post-marketing safety surveillance[J]. China Food & Grug Administration, 2021(11): 71-82.] DOI: 10.3969/j.issn.1673-5390.2021.11.009.

5.Litvinova O, Yeung AWK, Hammerle FP, et al. Digital technology applications in the management of adverse drug reactions: bibliometric analysis[J]. Pharmaceuticals (Basel), 2024, 17(3): 395. DOI: 10.3390/ph17030395.

6.Desai MK. Artificial intelligence in pharmacovigilance-opportunities and challenges[J]. Perspect Clin Res, 2024, 15(3): 116-121. DOI: 10.4103/picr.picr_290_23.

7.胡双. 关联规则挖掘研究及其在药物相互作用中的应用[D]. 昆明: 昆明理工大学, 2013.

8.Tang JY, Chuang LY, Hsi E, et al. Identifying the association rules between clinicopathologic factors and higher survival performance in operation-centric oral cancer patients using the Apriori algorithm[J]. Biomed Res Int, 2013, 2013: 359634. DOI: 10.1155/2013/359634.

9.Zhang CL, Xu R. Large-scale mining disease comorbidity relationships from post-market drug adverse events surveillance data[J]. BMC Bioinformatics, 2018, 19(Suppl 17): 500. DOI: 10.1186/s12859-018-2468-8.

10.Vougas K, Sakellaropoulos T, Kotsinas A, et al. Machine learning and data mining frameworks for predicting drug response in cancer: an overview and a novel in silico screening process based on association rule mining[J]. Pharmacol Ther, 2019, 203: 107395. DOI: 10.1016/j.pharmthera.2019.107395.

11.Naulaerts S, Meysman P, Bittremieux W, et al. A primer to frequent itemset mining for bioinformatics[J]. Brief Bioinform, 2015, 16(2): 216-231. DOI: 10.1093/bib/bbt074.

12.Guillet F, Hamilton H. Quality measures in data mining[M]. Heidelberg: Springer, 2006: 251-275.

13.Agrawal R, Imielinski T, Swami A, et al. Mining association rules between sets of items in large databases[J]. ACM SIGMOD, 1993, 22(2): 207-216. DOI: 10.1145/170035.170072.

14.Almenoff J, Tonning JM, Gould AL, et al. Perspectives on the use of data mining in pharmacovigilance[J]. Drug Saf, 2012, 28(11): 981-1007. DOI: 10.2165/00002018-200528110-00002.

15.Wang C, Guo XJ, Xu JF, et al. Exploration of the association rules mining technique for the signal detection of adverse drug events in spontaneous reporting systems[J]. PLoS One, 2012, 7(7): e40561. DOI: 10.1371/journal.pone.0040561.

16.Noguchi Y, Ueno A, Otsubo M, et al. A simple method for exploring adverse drug events in patients with different primary diseases using spontaneous reporting system[J]. BMC Bioinformatics, 2018, 19(1): 124. DOI: 10.1186/s12859-018-2137-y.

17.Noguchi Y, Nagasawa H, Tachi T, et al. Signal detection of oral drug-induced dementia in chronic kidney disease patients using association rule mining and Bayesian confidence propagation neural network[J]. Pharmazie, 2019, 74(9): 570-574. DOI: 10.1691/ph.2019.9426.

18.Wei L, Scott J. Association rule mining in the US Vaccine Adverse Event Reporting System (VAERS)[J]. Pharmacoepidem Dr S, 2015, 24(9): 922-933. DOI: 10.1002/pds.3797.

19.关瑜, 李雷, 朱旭, 等. 基于自发呈报系统的联合用药不良反应信号检测方法研究进展[J]. 中国药物评价, 2022, 39(6): 489-495. [Guan Y, Li L, Zhu X, et al. Research progress on detection method of adverse drug reaction signal detection method based on spontaneous reporting system[J]. Chinese Journal of Drug Evaluation, 2022, 39(6): 489-495.] DOI: 10.3969/j.issn.2095-3593.2022.06.009.

20.Shirakuni Y, Okamoto K, Kawashita N, et al. Signal detection of drug complications applying association rule learning for stevens-johnson syndrome[J]. J Comput Aided Chem, 2009, 10: 118-127. DOI: 10.2751/jcac.10.118.

21.Noguchi Y, Ueno A, Otsubo M, et al. A new search method using association rule mining for drug-drug interaction based on spontaneous report system[J]. Front Pharmacol, 2018, 9: 197. DOI: 10.3389/fphar.2018.00197.

22.陈帅. 基于关联规则分析的药品不良反应信号挖掘研究[D]. 南京: 南京邮电大学, 2021. DOI: 10.27251/d.cnki.gnjdc.2021.000869.

23.Harpaz R, Chase HS, Friedman C. Mining multi-item drug adverse effect associations in spontaneous reporting systems[J]. BMC Bioinformatics, 2010, 11(Suppl 9): S7. DOI: 10.1186/1471-2105-11-S9-S7.

24.Sindhu MS, Kannan B. Detecting signals of drug-drug interactions using association rule mining methodology[J]. IJCSIT, 2013, 4(4): 590-594. https://www.ijcsit.com/ijcsit-v4issue4.php.

25.Ibrahim H, Saad A, Abdo A, et al. Mining association patterns of drug-interactions using post marketing FDA's spontaneous reporting data[J]. J Biomed Inform, 2016, 60: 294-308. DOI: 10.1016/j.jbi.2016.02.009.

26.Cai RC, Liu M, Hu Y, et al. Identification of adverse drug-drug interactions through causal association rule discovery from spontaneous adverse event reports[J]. Artif Intell Med, 2017, 76: 7-15. DOI: 10.1016/j.artmed.2017.01.004.

27.Chasioti D, Yao XH, Zhang PY, et al. Mining directional drug interaction effects on myopathy using the FAERS database[J]. IEEE J Biomed Health Inform, 2019, 23(5): 2156-2163. DOI: 10.1109/JBHI.2018.2874533.

28.Harpaz R, Haerian K, Chase HS, et al. Statistical mining of potential drug interaction adverse effects in FDA's spontaneous reporting system[J]. AMIA Annu Symp Proc, 2010, 2010: 281-285. https://pubmed.ncbi.nlm.nih.gov/21346985/.

29.Yildirim P. Association patterns in open data to explore ciprofloxacin adverse events[J]. Appl Clin Inform, 2015, 6(4): 728-747. DOI: 10.4338/ACI-2015-06-RA-0076.

30.Matsumoto K, Nakao S, Hasegawa S, et al. Analysis of drug-induced interstitial lung disease using the Japanese Adverse Drug Event Report database[J]. SAGE Open Med, 2020, 8: 2050312120918264. DOI: 10.1177/2050312120918264.

31.Yogita, Sangma JW, Anal SRN, et al. Clustering-based hybrid approach for identifying quantitative multidimensional associations between patient attributes, drugs and adverse drug reactions[J]. Interdiscip Sci, 2020, 12(3): 237-251. DOI: 10.1007/s12539-020-00365-9.

32.付先军, 王振国, 李学博, 等. 基于关联网络挖掘的中药不良反应与药性相关关系分析及用药警戒探讨[J]. 世界科学技术-中医药现代化, 2019, 21(3): 355-360. [Fu XJ, Wang ZG, Li XB, et al. Correlation analysis of adverse drug reactions and drug properties based on association network mining: pharmacovigilance of Chinese medicine[J]. World Science and Technology/Modernization of Traditional Chinese Medicine and Materia Medica, 2019, 21(3): 355-360.] DOI: 10.11842/wst.2019.03.004.

33.陈莉, 陶渊达, 朱亚静, 等. 基于关联规则的中药注射剂及其成分的不良反应发生规律研究[J]. 中国合理用药探索, 2019, 16(8): 1-7, 16. [Chen L, Tao YD, Zhu YJ, et al. Study on occurrence rules of adverse drug reactions of traditional Chinese medicine injections and their components based on association rules[J]. Chinese Journal of Rational Drug Use, 2019, 16(8): 1-7, 16.] DOI: 10.3969/j.issn.2096-3327.2019.8.001.

34.王淳, 刘振丽, 宋志前, 等. 基于关联规则探索临床治疗心血管疾病方剂与其不良反应关系的研究[J]. 中国中医基础医学杂志, 2018, 24(10): 1478-1483. [Wang C, Liu ZL, Song ZQ, et al. Explore ration of the relation between chinese medicines for cardiovascular disease and clinical adverse effect based on association rules[J]. Journal of Basic Chinese Medicine, 2018, 24(10): 1478-1483.] DOI: 10.19945/j.cnki.issn.1006-3250.2018. 10.042.

35.崔盈盈, 吴嘉瑞, 谭迪, 等. 基于关联规则的参麦注射剂不良反应流行病学特点研究[J]. 药物流行病学杂志, 2018, 27(3): 160-168. [Cui YY, Wu JR, Tan D, et al. Study on the epidemiological characteristics of adverse reaction caused by shenmai injection based on association analysis[J]. Chinese Journal of Pharmacoepidemiology, 2018, 27(3): 160-168.] DOI: 10.19960/j.cnki.issn1005-0698.2018.03.004.

36.沈陈军, 宁萍, 闵芳芳, 等. 基于关联规则分析我院新的和严重药品不良反应发生规律[J]. 实用药物与临床, 2022, 25(10): 910-916. [Shen CJ, Ning P, Min FF, et al. Analysis of new and severe adverse drug reactions in our hospital based on association rules[J]. Practical Pharmacy And Clinical Remedies, 2022, 25(10): 910-916.] DOI: 10.14053/j.cnki.ppcr.202210011.

37.吴雨桐, 吴思佳, 杨建卫, 等. 基于Apriori算法分析2021年山东省医疗器械不良事件的关联性[J]. 山东大学学报（医学版）, 2022, 60(12): 111-118. [Wu YT, Wu SJ, Yang JW, et al. Association analysis of medical device adverse events in Shandong Province in 2021: apriori algorithm[J]. Journal of Shandong University (Health Sciences), 2022, 60(12): 111-118.] DOI: 10.6040/j.issn.1671-7554.0.2022.0574.

38.冯变玲, 杨世民, 贺小红, 等. 药品不良反应多维关联规则挖掘及预警模型构建[J]. 中国药事, 2012, 26(10): 1076-1082. [Feng BL, Yang SM, He XH, et al. Excavation of multidimensional association rules in adverse rug reaction and construction of warning model[J]. Chinese Pharmaceutical Affairs, 2012, 26(10): 1076-1082.] DOI: 10.16153/j.1002-7777.2012.10.020.

39.冯秀珍, 贺小红, 冯变玲. 基于关联规则的ADR预警系统及实证研究[J]. 科技管理研究, 2012, 32(9): 194-197. [Feng XZ, He XH, Feng BL. System and empirical study on adverse drug reaction warning based on association rule[J]. Science and Technology Management Research, 2012, 32(9): 194-197.] DOI: 10.3969/i.issn.1000-7695.2012.09.046.

40.叶明全, 苏洋, 童九翠. 基于多层关联规则挖掘的ADR风险检测与预警研究[J]. 池州学院学报, 2020, 34(3): 23-26. [Ye MQ, Su Y, Tong JC. Risk detection and early warning of adverse drug reactions based on multi-level association rule mining[J]. Journal of Chizhou University, 2020, 34(3): 23-26.] DOI: 10.13420/j.cnki.jczu.2020.03.007.

41.孙敏琦, 潘媛媛. 基于FP-Growth算法的精神障碍患者用药不良反应预警App研究[J]. 电脑知识与技术, 2022, 18(13): 132-134, 137. DOI: 10.14004/j.cnki.ckt.2022.0919.

42.Held F, Le Couteur DG, Blyth FM, et al. Polypharmacy in older adults: association rule and frequent-set analysis to evaluate concomitant medication use[J]. Pharmacol Res, 2017, 116: 39-44. DOI: 10.1016/j.phrs.2016.12.018.

43.王琚丽, 张冰, 吕锦涛, 等. 基于真实世界的基层医院心血管系统疾病中西药联合应用情况研究[J]. 中国医院用药评价与分析, 2022, 22(1): 83-88, 92. [Wang JL, Zhang B, Lyu JT, et al. Combination of traditional chinese and western medicine in the treatment of cardiovascular diseases in primary hospitals based on real world[J]. Evaluation and Analysis of Drug-Use in Hospitals of China, 2022, 22(1): 83-88, 92.] DOI: 10.14009/j.issn.1672-2124.2022.01.017.

44.吴丽娜, 谢雁鸣, 刘峘, 等. 基于关联规则Apriori算法的真实世界中结肠恶性肿瘤患者的中西药联合应用特征研究 [J]. 中药药理与临床, 2019, 35(5): 138-143. [Wu LN, Xie YM, Liu H, et al. Real-world study on the characteristics of combined application of Chinese and western medicine in the treatment for colonic malignant tumors by association rules analysis[J]. Pharmacology and Clinics of Chinese Materia Medica, 2019, 35(5): 138-143.] DOI: 10.13412/j.cnki.zyyl.2019.05.030.

45.杨会生, 谢雁鸣, 陈岑, 等. 基于关联规则分析的“真实世界”复方苦参注射液治疗肺癌中西药联合应用特征研究[J]. 中国中药杂志, 2018, 43(8): 1708-1713. [Yang HS, Xie YM, Chen C, et al. Association rules analysis of Fufang Kushen injection in combination with modern medications in treating lung cancer: real-world study based on hospital information[J]. China Journal of Chinese Materia Medica, 2018, 43(8): 1708-1713.] DOI: 10.19540/j.cnki.cjcmm.20180125.007.

46.Nishtala PS, Chyou TY, Held F, et al. Association rules method and big data: Evaluating frequent medication combinations associated with fractures in older adults[J]. Pharmacoepidemiol Drug Saf, 2018, 27(10): 1123-1130. DOI: 10.1002/pds.4432.

47.Nishtala PS, Chyou TY. Identifying drug combinations associated with acute kidney injury using association rules method[J]. Pharmacoepidemiol Drug Saf, 2020, 29(4): 467-473. DOI: 10.1002/pds.4960.

48.Chyou TY, Nishtala PS. Identifying frequent drug combinations associated with delirium in older adults: application of association rules method to a case-time-control design[J]. Pharmacoepidemiol Drug Saf, 2021, 30(10): 1402-1410. DOI: 10.1002/pds.5292.

49.Chen W, Yang J, Wang HL, et al. Discovering associations of adverse events with pharmacotherapy in patients with non-small cell lung cancer using modified apriori algorithm[J]. Biomed Res Int, 2018, 2018: 1245616. DOI: 10.1155/2018/1245616.

50.Yamamoto H, Kayanuma G, Nagashima T, et al. Early Detection of adverse drug reaction signals by association rule mining using large-scale administrative claims data[J]. Drug Saf, 2023, 46(4): 371-389. DOI: 10.1007/s40264-023-01278-4.