Data mining methods have been widely applied throughout the entire process of real-world study, including data exploration, pattern mining, predictive decision-making, and bias control. This paper presents the first systematic summary of the commonly used data mining methods in real-world study, focusing on ten common methods such as association rules, cluster analysis, factor analysis, artificial neural networks, propensity scores, decision trees, Bayesian networks, Logistic regression, and latent structure models. It systematically reviews their definitions, implementation paths, and combines them with specific scenarios of real-world study to deeply analyze the suitability and application efficiency of each method. This paper systematically summarizes the commonly used data mining methods in real-world study, clarifies the application boundaries and advantages of different methods in real-world study, provides theoretical references and practical guidance for the scientific selection and standardized application of data mining methods in real-world study, and helps promote the standardized development and innovative integration of data mining technology in the field of real-world study, laying the foundation for related research.

1. 郭方达, 李京儒, 胡洁, 等. 基于真实世界数据的可穿戴监护设备在门急诊患者中的临床价值研究[J]. 中国医学装备, 2025, 22(8): 11-16. [Guo FD, Li JR, Hu J, et al. A study on the clinical value of a wearable monitoring device based on real-world data in patients of outpatient and emergent department[J]. China Medical Equipment, 2025, 22(8): 11-16.] DOI: 10.3969/j.issn.1672-8270.2025.08.003.

2. 江旻, 袁延楠, 刘晓红, 等. 基于电子病历的抗肿瘤药物真实世界外对照臂研究适用性评价框架的考量与建立[J]. 中国医院药学杂志, 2024, 44(12): 1457-1462. [Jiang M, Yuan YN, Liu XH, et al. Practices and considerations for feasibility evaluations of real-world external control arm for antineoplas-tic drugs based upon electronic medical records[J]. Chinese Journal of Hospital Pharmacy, 2024, 44 (12): 1457-1462.] DOI: 10.13286/j.1001-5213.2024.12.15.

3. Lavertu A, Vora B, Giacomini KM, et al. A new era in pharmacovigilance: toward real-world data and digital monitoring[J]. Clin Pharmacol Ther, 2021, 109(5): 1197-1202. DOI: 10.1002/cpt.2172.

4. U.S. Congress. H.R.34-21st century cures act.114th congress (2015-2016)[S/OL]. (2016-12-13) [2025-10-05]. https://www.congress.gov/bill/114th-congress/house-bill/34.

5. FDA. Use of electronic health record data in clinical investigations[R/OL]. (2018-07-19) [2025-10-05]. https://www.fda.gov/media/97567/download.

6. U.S. Department of Health and Human Services, Food and Drug Administration, Center for Drug Evaluation and Research, Center for Biologics Evaluation and Research, Oncology Center for Excellence. Real-world data: assessing electronic health records and medical claims data to support regulatory decision-making for drug and biological products: guidance for industry[R/OL]. (2024-07-01) [2025-10-05]. https://www.fda.gov/regulatory-information/search-fda-guidance-documents/real-world-data-assessing-electronic-health-records-and-medical-claims-data-support-regulatory.

7. 国家药品监督管理局. 真实世界证据支持药物研发与审评的指导原则（试行）[EB/OL]. (2020-01-07) [2021-12-16]. https://www.nmpa.gov.cn/xxgk/ggtg/ypggtg/ypqtggtg/2020010715 1901190.html.

8. Little RJ, D'Agostino R, Cohen ML, et al. The prevention and treatment of missing data in clinical trials[J]. N Engl J Med, 2012, 367(14): 1355-1360. DOI: 10.1056/NEJMsr1203730.

9. 韩梅, 郭蓉娟, 夏芸. 真实世界注册研究设计要素及伦理的研究进展[J]. 重庆医学, 2025, 54(10): 2431-2436. [Han M, Guo RJ, Xia Y. Research progress on the design elements and ethics of real-world registry studies[J]. Chongqing Medicine, 2025, 54(10): 2431-2436.] DOI: 10.3969/j.issn.1671-8348.2025.10.034.

10. 朱凌云, 吴宝明, 曹长修. 医学数据挖掘的技术、方法及应用[J].生物医学工程学杂志, 2003, 20(3): 559-562. [Zhu LY, Wu BM, Cao CX. Introduction to medical data mining[J]. Journal of Biomedical Engineering, 2003, 20(3): 559-562.] DOI: 10.3321/j.issn:1001-5515.2003.03.047.

11. Tseng MH, Wu HC. Investigating health equity and healthcare needs among immigrant women using the association rule mining method[J]. Healthcare (Basel), 2021, 9(2): 195. DOI: 10.3390/healthcare9020195.

12. 毕建欣, 张岐山. 关联规则挖掘算法综述[J]. 中国工程科学, 2005, 7(4): 88-94. [Bi JX, Zhang QS. Survey of the algorithms on association rule mining[J]. Engineering Science, 2005, 7(4): 88-94.] DOI: 10.3969/j.issn.1009-1742.2005.04.016.

13. Kim SS, Kim HS. The impact of the association between cancer and diabetes mellitus on mortality[J]. Pers Med, 2022, 12(7): 1099.DOI: 10.3390/jpm12071099.

14. 施雪清, 沈锡容, 熊梦欣, 等. 基于医院信息系统数据挖掘真实世界多囊卵巢综合征中药用药规律[J]. 新中医, 2024, 56(20): 15-20. [Shi XQ, Shen XR, Xiong MX, et al. Study on Chinese medicinal medication rules of treating polycystic ovary syndrome based on real-world data mining of hospital information system[J]. Journal of New Chinese Medicine, 2024, 56 (20): 15-20.] DOI: 10.13457/j.cnki.jncm.2024.20.003.

15. Oyewole GJ, Thopil GA. Data clustering: application and trends[J]. Artif Intell Rev, 2023, 56(7): 6439-6475. DOI: 10.1007/s10462-022-10325-y.

16. 章永来, 周耀鉴. 聚类算法综述[J]. 计算机应用, 2019, 39(7): 1869-1882. [Zhang YL, Zhou YJ. Review of clustering algorithms[J]. Journal of Computer Applications, 2019, 39(7): 1869-1882.] DOI: 10.11772/j.issn.1001-9081.2019010174.

17. 董芷欣. 基于Python聚类分析的聚类数确定方法对比[J]. 微型电脑应用, 2023, 39(12): 220-223. [Dong ZX. Comparative analysis of cluster number determination based on python cluster analysis[J]. Microcomputer Applications, 2023, 39(12): 220-223.] DOI: 10.3969/j.issn.1007-757X.2023.12.057.

18. 张迪, 陈艺幻, 张伟娜. 常用关联与聚类分析方法对中医处方数据的适用性探讨[J]. 中草药, 2025, 56(11): 3974-3984. [Zhang D, Chen YH, Zhang WN. Discussion on applicability of common association and cluster analysis methods to traditional Chinese medicine prescription data[J]. Chinese Traditional and Herbal Drugs, 2025, 56(11): 3974-3984.] DOI: 10.7501/j.issn.0253-2670.2025.11.018.

19. 李家劼, 申国明, 刘金星, 等. 基于因子分析与聚类分析的糖尿病周围神经病变合并血脂异常中医证候学特征研究[J]. 中华中医药杂志, 2024, 39(11): 6132-6139. [Li JJ, Shen GM, Liu JX, et al. Research on traditional Chinese medicine syndrome characteristics of diabetes peripheral neuropathy with dyslipidemia based on factor analysis and cluster analysis[J]. China Journal of Traditional Chinese Medicine and Pharmacy, 2024, 39(11): 6132-6139.] https://d.wanfangdata.com.cn/periodical/CiBQZXJpb2RpY2FsQ0hJU29scjkyMDI2MDMwNjE2NTI1NxIPemd5eXhiMjAyNDExMDk1Ggh1NzVobnd0NQ%3D%3D.

20. Min Q, Wu Z, Yao J, et al. Association between atherogenic index of plasma control level and incident cardiovascular disease in middle-aged and elderly Chinese individuals with abnormal glucose metabolism[J]. Cardiovasc Diabetol, 2024, 23(1): 54. DOI: 10.1186/s12933-024-02144-y.

21. （美）金在温, 查尔斯·W·米勒著，叶华译. 因子分析统计方法与应用问题[M]. 上海: 格致出版社, 2023: 1-5.

22. 吕咪, 车慧, 周秉舵, 等. 基于因子分析和聚类分析的800例非糜烂性反流病与上腹痛综合征胃肠症状重叠患者中医证型横断面研究[J]. 中国中医药信息杂志, 2025, 32(9): 141-148. [Lyu M, Che H, Zhou BD, et al. Cross-sectional study on TCM syndromes of 800 patients with overlapping gastrointestinal symptoms of NERD and EPS based on factor analysis and clustering analysis[J]. Chinese Journal of Information on Traditional Chinese Medicine, 2025, 32(9): 141-148.] DOI: 10.19879/j.cnki.1005-5304.202503321.

23. 江雯婷, 黄嬿, 常昕楠, 等. 基于药性理论及因子分析法分析中药饮片治疗IgA-肾病的用药规律[J]. 中国医药导刊, 2022, 24(9): 843-849. [Jiang WT, Huang Y, Chang XN, et al. Analysis of the medication rule of Chinese herbal decoction pieces in the treatment of IgA nephropathy based on Chinese herbal property theory and factor analysis[J]. Chinese Journal of Medical Guide, 2022, 24(9): 843-849.] DOI: 10.3969/j.issn.1009-0959. 2022.09.002.

24. 香港科技大学. 隐结构模型与中医证候研究[EB/OL]. (2014-12-05) [2024-12-11]. https://www.cse.ust.hk/lzhang/tcm/resource.html.

25. 李宇迪, 丁樱, 徐炎, 等. 基于隐结构模型的《幼科发挥》“药-证-方”规律[J]. 世界中医药, 2025, 20(4): 606-612. [Li YD, Ding Y, Xu Y, et al. Rules of "medicine-syndrome-prescription" in Youke Fahui based on latent structure model[J]. World Chinese Medicine, 2025, 20(4): 606-612.] https://link.cnki.net/urlid/11.5529.R.20250506.1231.014.

26. 袁君, 丁霄, 徐洁, 等. 基于隐结构模型和关联规则探讨非酒精性脂肪肝病的用药规律[J]. 世界中西医结合杂志, 2025, 20(7): 1303-1311. [Yuan J, Ding X, Xu J, et al. Medication patterns in non-alcoholic fatty liver disease based on latent struc-ture model and association rules[J]. World Journal of Integrated Traditional and Western Medicine, 2025, 20(7): 1303-1311.] DOI: 10.13935/j.cnki.sjzx.250705.

27. 李伟珂, 伊明洋, 倪园园, 等. 基于潜在类别结合隐结构模型的慢性阻塞性肺疾病肺气虚证分级量化诊断研究[J]. 中医杂志, 2025, 66(7): 710-716. [Li WK, Yi MY, Ni YY, et al. Study on graded quantitative diagnosis of lung qi deficiency syndrome in chronic obstructive pulmonary disease based on latent class analysis combined with hidden structure model[J]. Journal of Traditional Chinese Medicine, 2025, 66 (7): 710-716.] DOI: 10.13288/j.11-2166/r.2025.07.010.

28. 李俊雅, 史阳琳, 杨建雅, 等. 基于真实世界数据探讨中医药治疗非小细胞肺癌的用药规律[J]. 药物流行病学杂志, 2025, 34(4): 398-409. [Li JY, Shi YL, Yang JY, et al. Exploration on medication pattern of traditional Chinese medicine treatment for non-small cell lung cancer based on real world data[J]. Chinese Journal of Pharmacoepidemiology, 2025, 34(4): 398-409.] DOI: 10.12173/j.issn.1005-0698.202408072.

29. Rosenbaum PR, Rubin DB. The central role of the propensity score in observational studies for causal effects[J]. Biometrika, 1983, 70(1): 41-55. DOI: 10.1093/biomet/70.1.41.

30. 岳青青, 焦志刚, 凡如, 等. 倾向性评分法简介及其SAS实现[J]. 中国卫生统计, 2021, 38(1): 144-147. [Yue QQ, Jiao ZG, Fan R, et al. Introduction to propensity score method and its implementation in SAS[J]. Chinese Journal of Health Statistics, 2021, 38(1): 144-147.] DOI: 10.3969/j.issn.1002-3674. 2021.01.038.

31. 施婷婷, 刘振球, 袁黄波, 等. 倾向性评分匹配法在非随机对照研究中的应用[J]. 中国卫生统计, 2021, 38(2): 312-314. [Shi TT, Liu ZQ, Yuan HB, et al. The application of propensity score matching method in non-randomized controlled studies[J]. Chinese Health Statistics, 2021, 38(2): 312-314.] DOI: 10.3969/j.issn.1002-3674.2021.02.040.

32. 袁博寒, 李亚妤, 杨亚珍. 基于真实世界的百令片治疗慢性肾脏病疗效评价[J]. 中国现代应用药学, 2024, 41(23): 3316-3321. [Yuan BH, Li YY, Yang YZ. Efficacy evaluation of Bailing tablets in the treatment of chronic kidney disease based on real-world data[J]. Chinese Journal of Modern Applied Pharmacy, 2024, 41(23): 3316-3321.] DOI: 10.13748/j.cnki.issn1007-7693.20242926.

33. 朱金霞, 刘光伟, 张小瑞, 等. 基于倾向性评分的真实世界扶正散结方治疗乙肝相关肝癌的疗效评价[J]. 中医肿瘤学杂志, 2023, 5(6): 7-16. [Zhu JX, Liu GW, Zhang XR, et al. Evaluation of the real-world therapeutic effect of Fuzheng Sanjie prescription for hepatitis B related liver cancer based on propensity score[J]. Journal of Oncology in Chinese Medicine, 2023, 5(6): 7-16.] DOI: 10.19811/j.cnki.ISSN2096-6628.2023.11.002.

34. 高洪阳, 赵阳, 盛松. 基于真实世界观察参附注射液辅助治疗心力衰竭的临床疗效[J]. 中西医结合心脑血管病杂志, 2023, 21(20): 3696-3702. [Gao HY, Zhao Y, Sheng S. The clinical effect of shenfu injection in the adjuvant treatment of heart failure based on real world[J]. Chinese Journal of Integrative Medicine on Cardio/Cerebrovascular Disease, 2023, 21(20): 3696-3702.] DOI: 10.12102/j.issn.1672-1349.2023.20.004.

35. 成冯镜茗, 谢雁鸣, 厉将斌, 等. 基于医疗电子数据的脉络舒通丸对疑似胃肠道不良反应影响的真实世界研究[J]. 中国中医基础医学杂志, 2022, 28(9): 1474-1479. [Cheng FJM, Xie YM, Li JB, et al. Real-world study on the effect of Mailuo Shutong pill on suspected gastrointestinal adverse reactions based on medical electronic data[J]. Chinese Journal of Basic Medicine in Traditional Chinese Medicine, 2022, 28(9): 1474-1479.] DOI: 10.19945/j.cnki.issn.1006-3250.2022.09.015.

36. 张棪, 曹健. 面向大数据分析的决策树算法[J]. 计算机科学, 2016, 43(S1): 374-379, 383. [Zhang Y, Cao J. Decision tree algorithm for big data analysis[J]. Computer Science, 2016, 43(S1): 374-379, 383.] https://d.wanfangdata.com.cn/periodical/CiBQZXJpb2RpY2FsQ0hJU29scjkyMDI2MDMwNjE2NTI1NxIOanNqa3gyMDE2ejEwODkaCHl3ajQzZndh.

37. 马红丽, 徐长英, 杨新鸣. 决策树模型在中医药领域的应用现状[J]. 世界中医药, 2021, 16(17): 2648-2651, 2656. [Ma HL, Xu CY, Yang XM. Application status of decision tree model in the field of traditional Chinese medicine[J]. World Chinese Medicine, 2021, 16(17): 2648-2651, 2656.] DOI: 10.3969/j.issn.1673-7202. 2021.17.025.

38. Nielsen ML, Petersen TC, Maul LV, et al. Predicting psoriatic arthritis in psoriasis patients-a Swiss registry study[J]. J Psoriasis Psoriatic Arthritis, 2024, 9(2): 41-50. DOI: 10.1177/2475530323 1217492.

39. 石玉琳, 刘嘉懿, 胡晓娟, 等. 基于舌脉象数据的决策树算法的非小细胞肺癌证候分类方法[J]. 世界科学技术-中医药现代化, 2022, 24(7): 2766-2775. [Shi YL, Liu JY, Hu XJ, et al. Classification of syndromes of non-small cell lung cancer based on decision tree algorithm based on tongue data and pulse data[J]. World Science and Technology-Modernization of Traditional Chinese Medicine, 2022, 24(7): 2766-2775.] DOI: 10.11842/wst.20210705003.

40. 辛雅雯, 卓玛层, 师永兰, 等. 基于真实世界的治疗耐碳青霉烯病原菌抗菌药物方案的药物经济学评价[J]. 中国药物经济学, 2024, 19(3): 5-10. [Xin YW, Zhuo MC, SHI YL, et al. Pharmacoeconomic evaluation of antimicrobial treatment for carbapenem-resistant organism according to real world data[J]. China Journal of Pharmaceutical Economics, 2024, 19(3): 5-10.] DOI: 10.12010/j.issn.1673-5846.2024.03.001.

41. 陈炳为, 主编. 医学统计学, 第4版[M]. 南京: 南京东南大学出版社, 2023: 268.

42. 韩文杰, 朱明军, 王新陆, 等. 冠心病稳定型心绞痛患者再入院中西医风险预测模型构建与应用评估——基于真实世界临床数据的前瞻性研究[J]. 中医杂志, 2025, 66(6): 604-611. [Han WJ, Zhu MJ, Wang XL, et al. Construction and application evaluation of integrated traditional Chinese and Western medicine risk prediction model for readmission of patients with stable angina pectoris of coronary heart disease-a prospective study based on real-world clinical data[J]. Journal of Traditional Chinese Medicine, 2025, 66(6): 604-611.] DOI: 10.13288/j.11-2166/r.2025.06.010.

43. 刘兴兴, 黎元元, 魏戌, 等. 鹿瓜多肽注射液治疗3 071例骨关节炎真实世界疗效分析[J]. 世界中医药, 2021, 16(7): 1126-1133. [Liu XX, Li YY, Wei X, et al. Efficacy analysis of Lugua polypeptide injection in the treatment of 3,071 cases of osteoarthritis in real world[J]. World Chinese Medicine, 2021, 16(7): 1126-1133.] DOI: 10.3969/j.issn.1673-7202.2021.07.020.

44. Kitson NK, Constantinou AC, Guo Z, et al. A survey of Bayesian network structure learning[J]. Artif Intell Rev, 2023, 56: 8721-8814. DOI: 10.1007/s10462-022-10351-w.

45. Beresniak A, Bertherat E, Perea W, et al. A Bayesian network approach to the study of historical epidemiological databases: modelling meningitis outbreaks in the Niger[J]. Bull World Health Organ, 2012, 90(6): 412-417A. DOI: 10.2471/BLT.11.086009.

46. Qi H, Liu R, Dong CC, et al. Identifying influencing factors of metabolic syndrome in patients with major depressive disorder: a real-world study with Bayesian network modeling[J]. J Affect Disord, 2024, 362: 308-316. DOI: 10.1016/j.jad.2024.07.004.

47. 陈玲, 郝志梅, 魏霞霞, 等. 基于贝叶斯网络的老年人失能风险预测模型构建[J]. 中国老年学杂志, 2023, 43(22): 5596-5600. [Chen L, Hao ZM, Wei XX, et al. Construction of disability risk prediction model for the elderly based on Bayesian network[J]. Chinese Journal of Gerontology, 2023, 43(22): 5596-5600.] DOI: 10.3969/j.issn.1005-9202.2023.22.059.

48. 常强, 赵伟, 赵仰杰. 基于神经网络的数据分类预测与实现 [J].软件, 2018, 39(12): 207-209. [Chang Q, Zhao W, Zhao YJ. Prediction and implementation of data classification based on neural network[J]. Computer Engineering & Software, 2018, 39(12): 207-209.] DOI: 10.3969/j.issn.1003-6970.2018.12.047.

49. 许朝霞, 王忆勤, 颜建军, 等. 基于支持向量机和人工神经网络的心血管疾病中医证候分类识别研究[J]. 北京中医药大学学报, 2011, 34(8): 539-543. [Xu CX, Wang YQ, Yan JJ, et al. Study on classification and identification of TCM syndromes of cardiovascular diseases based on support vector machine and artificial neural network[J]. Journal of Beijing University of Chinese Medicine, 2011, 34(8): 539-543.] https://d.wanfangdata.com.cn/periodical/CiBQZXJpb2RpY2FsQ0hJU29scjkyMDI2MDMwNjE2NTI1NxISYmp6eXlkeHhiMjAxMTA4MDA5GghpY2I4ZDc5Ng%3D%3D.

50. 郭红霞, 师义民. 中医脉象的BP神经网络分类方法研究[J]. 计算机工程与应用, 2005, 41(32): 187-189. [Guo HX, Shi YM. Research on BP neural network classification method for TCM pulse conditions[J]. Computer Engineering and Applications, 2005, 41(32): 187-189.] DOI: 10.3321/j.issn:1002-8331.2005.32.059.

51. 李秋华, 史国峰, 李玥博, 等. 基于卷积神经网络的“舌边白涎”舌象识别研究[J]. 湖南中医药大学学报, 2024, 44(7): 1254-1260. [Li QH, Shi GF, Li YB, et al. Research on tongue image recognition of "white saliva on tongue edge" based on convolutional neural network[J]. Journal of Hunan University of Chinese Medicine, 2024, 44(7): 1254-1260.] DOI: 10.3969/j.issn.1674-070X.2024.07.016.

52. 罗爱静, 王哲轩, 谢文照, 等. 基于神经网络的甲状腺肿瘤复发风险评估模型[J]. 中国医学物理学杂志, 2025, 42(7): 974-980. [Luo AJ, Wang ZX, Xie WZ, et al. Risk assessment model for thyroid tumor recurrence based on neural network[J]. Chinese Journal of Medical Physics, 2025, 42(7): 974-980.] DOI: 10.3969/j.issn.1005-202X.2025.07.020.

53. 叶桦, 何黎, 胡远樟, 等. 基于卷积神经网络的2型糖尿病证候分布演化规律研究[J]. 时珍国医国药, 2021, 32(6): 1522-1524. [Ye H, He L, Hu YZ, et al. Study on the evolution law of TCM syndrome distribution in type 2 diabetes mellitus based on convolutional neural network[J]. Lishizhen Medicine and Materia Medica Research, 2021, 32(6): 1522-1524.] DOI: 10.3969/j.issn.1008-0805.2021.06.70.

54. 王吉庆, 张蕾, 徐世芬, 等. 基于机器学习的真实世界健康人舌象与年龄及性别相关性研究[J]. 世界科学技术-中医药现代化, 2024, 26(11): 2806-2814. [Wang JQ, Zhang L, Xu SF, et al. Study on the correlation between machine learning-based tongue features of healthy individuals in the real world and age and gender[J]. World Science and Technology-Modernization of Traditional Chinese Medicine, 2024, 26(11): 2806-2814.] DOI: 10.11842/wst.20240115012.

55. 刘丽娜, 宋奇修, 张伦, 等. 真实世界下直接口服抗凝药药物相关问题的Lasso-Logistic回归分析及列线图预测模型构建[J]. 实用药物与临床, 2025, 28(6): 406-412. [Liu LN, Song QX, Zhang L, et al. Lasso-Logistic regression analysis of drug-related problems of direct oral anticoagulants in real world and construction of nomogram prediction model[J]. Practical Pharmacy and Clinical Remedies, 2025, 28(6): 406-412.] DOI: 10.14053/j.cnki.ppcr.202506002.

56. 刘晓涵, 郑宇钰, 李瑞雪, 等. 基于LASSO回归分析的糖尿病肾病风险预测模型的构建[J]. 中国社会医学杂志, 2025, 42(3): 363-368. [Liu XH, Zheng YY, Li RX, et al. Construction of risk prediction model for diabetic nephropathy based on LASSO regression analysis[J]. Chinese Journal of Social Medicine, 2025, 42(3): 363-368.] DOI: 10.3969/j.issn.1673-5625.2025.03.023.

57. Liu S, Yuan Z, Rao S, et al. Adverse drug reactions related to methotrexate: a real-world pharmacovigilance study using the FAERS database from 2004 to 2024[J]. Front Immunol, 2025, 16: 1586361. DOI: 10.3389/fimmu.2025.1586361.

58. 郭春彦, 柳静, 张萌, 等. 基于真实世界数据的青春期异常子宫出血用药规律研究[J]. 世界中医药, 2024, 19(10): 1469-1475, 1485. [Guo CY, Liu J, Zhang M, et al. Medication rules of TCM for abnormal uterine bleeding during puberty based on real world data mining[J]. World Chinese Medicine, 2024, 19(10): 1469-1475, 1485.] DOI: 10.3969/j.issn.1673-7202.2024.10.016.

59. 杨绚如, 薛晓鸥, 朱玉莹, 等. 丹白颗粒治疗盆腔炎性疾病后遗症的成本-效果分析[J]. 世界中医药, 2024, 19(12): 1820-1825. [Yang XR, Xue XO, Zhu YY, et al. Cost-effectiveness analysis of Danbai granules in the treatment of sequelae of pelvic inflammatory disease[J]. World Chinese Medicine, 2024, 19(12): 1820-1825.] DOI: 10.3969/j.issn.1673-7202.2024.12.018.

60. 李妍. 基于贝叶斯网络和BP神经网络预测用户使用App行为的分析与研究[D]. 北京: 北京邮电大学, 2018.

61. 葛永彬, 董剑平. 利用医疗大数据开展真实世界临床研究的合规性要求[J]. 中国食品药品监管, 2023(10): 86-94.[Ge YB, Dong JP. Compliance requirements for real-world clinical research using medical big data[J]. China Food and Drug Administration Magazine, 2023(10): 86-94.] DOI: 10.3969/j.issn.1673-5390.2023.10.010.

62. 弓孟春, 陆亮. 医学大数据研究进展及应用前景[J].医学信息学杂志, 2016, 37(2): 9-15. [Gong MC, Lu L. Research progress and application prospect of medical big data[J]. Journal of Medical Informatics, 2016, 37(2): 9-15.] DOI: 10.3969/j.issn.1673-6036. 2016.02.002.

63. 袁培江, 苏峰. 两个黑箱问题——深度神经网络和脑神经网络[J]. 科技导报, 2017, 35(18): 12. [Yuan PJ, Su F. Two black box problems: deep neural network and brain neural network[J]. Science & Technology Review, 2017, 35(18): 12.] https://d.wanfangdata.com.cn/periodical/CiBQZXJpb2RpY2FsQ0hJU29scjkyMDI2MDMwNjE2NTI1NxINa2pkYjIwMTcxODAwNhoIcml4Z2h1MjY%3D.

64. 卢存存, 陈子佳, 张强, 等. 基于真实世界数据的目标试验模拟研究: 现状与展望[J]. 中国循证医学杂志, 2023, 23(4): 492-496. [Lu CC, Chen ZJ, Zhang Q, et al. Target trial emulation based on real-world data: current status and prospects[J]. Chinese Journal of Evidence-Based Medicine, 2023, 23(4): 492-496.] https://kns.cnki.net/kcms2/article/abstract?v=W-y7fKBVJCzVfuhoDu5QtfJMfeMFXKoylM1sR3vZrLEzXogOJp7yGLqjQfF4BD9uZHq_hcQQfKtoIgI7450rXbJ0CPoCkJc1vy0WJoOZgIoEz528tKhMpDQpwfWP6QDHiGpvGFXdxk8glM30U_ffC8k8BcwoqO5vdViHnrAiFdcLyL0wI73R4g==&uniplatform=NZKPT&language=CHS.

65. Schuler MS, Rose S. Targeted maximum likelihood estimation for causal inference in observational studies[J]. Am J Epidemiol, 2017, 185(1): 65-73. DOI: 10.1093/aje/kww165.

66. 宋雨昕, 叶倩, 赵盟生, 等. 疾病风险动态预测模型方法前沿进展与精准预防[J]. 科技导报, 2024, 42(12): 75-91. [Song YX, Ye Q, Zhao MS, et al. Frontier progress and precise prevention of dynamic disease risk prediction model methods[J]. Science & Technology Review, 2024, 42(12): 75-91.] DOI: 10.3981/j.issn.1000-7857.2024.05.00543.

67. Yoo SK, Fitzgerald CW, Cho BA, et al. Prediction of checkpoint inhibitor immunotherapy efficacy for cancer using routine blood tests and clinical data[J]. Nat Med, 2025, 31(3): 869-880. DOI: 10.1038/s41591-024-03398-5.

68. Jafleh EA, Alnaqbi FA, Almaeeni HA, et al. The role of wearable devices in chronic disease monitoring and patient care: a comprehensive review[J]. Cureus, 2024, 16(9): e68921. DOI: 10.7759/cureus.68921.

69. 余新艳, 赵旭东, 赵晓晔, 等. 基于社区移动医疗的心律失常筛查方案真实世界研究[J]. 中国全科医学, 2023, 26(2): 192-200, 209. [Yu XY, Zhao XD, Zhao XY, et al. Real-world study on arrhythmia screening program based on community mobile health[J]. Chinese General Practice, 2023, 26(2): 192-200, 209.] DOI: 10.12114/j.issn.1007-9572.2022.0511.