[1] Concato J, Corrigan-Curay J. Real-world evidence-Where are we now? [J]. The New England Journal of Medicine, 2022, 386(18): 1680-1682.
[2] Corrigan-Curay J, Sacks L, Woodcock J. Real-world evidence and real-world data for evaluating drug safety and effectiveness[J]. The Journal of the American Medical Association, 2018, 320(9): 867-868.
[3] Basch E, Schrag D. The evolving uses of "real-world" data[J]. The Journal of the American Medical Association, 2019, 321(14): 1359-1360.
[4] Sherman R E, Anderson S A, dal Pan G J, et al. Real-world evidence-what is it and what can it tell us? [J]. The New England Journal of Medicine, 2016, 375(23): 2293-2297.
[5] Makady A, de Boer A, Hillege H, et al. What is real-world data? A review of definitions based on literature and stakeholder interviews[J]. Value Health, 2017, 20(7): 858-865.
[6] Coorevits P, Sundgren M, Klein G O, et al. Electronic health records: New opportunities for clinical research[J]. Journal of Internal Medicine, 2013, 274(6): 547-560.
[7] Kotecha D, Asselbergs F W, Achenbach S, et al. CODE-EHR best practice framework for the use of structured electronic healthcare records in clinical research[J]. The British Medical Journal, 2022, 378: e069048.
[8] Wood A, Denholm R, Hollings S, et al. Linked electronic health records for research on a nationwide cohort of more than 54 million people in England: Data resource[J]. The British Medical Journal, 2021, 373: 826.
[9] Girman C J, Ritchey M E, Lo R V. Real-world data: Assessing electronic health records and medical claims data to support regulatory decision-making for drug and biological products[J]. Pharmacoepidemiology and Drug Safety, 2022, 31(7): 717-720.
[10] Brown J P, Wing K, Evans S J, et al. Use of real-world evidence in postmarketing medicines regulation in the European Union: A systematic assessment of European Medicines Agency referrals 2013—2017[J]. BMJ Open, 2019, 9(10): e028133.
[11] Eskola S M, Leufkens H G M, Bate A, et al. Use of realworld data and evidence in drug development of medicinal products centrally authorized in Europe in 2018—2019[J]. Clinical Pharmacology & Therapeutics, 2022, 111(1): 310-320.
[12] Flynn R, Plueschke K, Quinten C, et al. Marketing authorization applications made to the European medicines agency in 2018—2019: What was the contribution of real-world evidence? [J]. Clinical Pharmacology & Therapeutics, 2022, 111(1): 90-97.
[13] McNair D, Lumpkin M, Kern S, et al. Use of RWE to inform regulatory, public health policy, and intervention priorities for the developing world[J]. Clinical Pharmacology & Therapeutics, 2022, 111(1): 44-51.
[14] Honig P K. The "coming of age" of real-world evidence in drug development and regulation[J]. Clinical Pharmacology & Therapeutics, 2022, 111(1): 11-14.
[15] Breckenridge A M, Breckenridge R A, Peck C C. Report on the current status of the use of real-world data(RWD) and real-world evidence (RWE) in drug development and regulation[J]. British Journal of Clinical Pharmacology, 2019, 85(9): 1874-1877.
[16] Booth C M, Karim S, Mackillop W J. Real-world data: Towards achieving the achievable in cancer care[J]. Nature Reviews Clinical Oncology, 2019, 16(5): 312-325.
[17] Wu J, Wang C, Toh S, et al. Use of real-world evidence in regulatory decisions for rare diseases in the United States-Current status and future directions[J]. Pharmacoepidemiology and Drug Safety, 2020, 29(10): 1213-1218.
[18] Nolan C M, Polgar O, Schofield S J, et al. Pulmonary rehabilitation in idiopathic pulmonary fibrosis and COPD: A propensity-matched real-world study[J]. Chest, 2022,161(3): 728-737.
[19] Heerspink H J L, Karasik A, Thuresson M, et al. Kidney outcomes associated with use of SGLT2 inhibitors in real-world clinical practice (CVD-REAL 3): A multinational observational cohort study[J]. Lancet Diabetes Endocrinology, 2020, 8(1): 27-35.
[20] Khan M S, Butler J, Greene S J. The real world of de novo heart failure: The next frontier for heart failure clinical trials?[J]. European Journal of Heart Failure, 2020, 22(10): 1786-1789.
[21] Pettus J H, Zhou F L, Shepherd L, et al. Differences between patients with type 1 diabetes with optimal and suboptimal glycaemic control: A real-world study of more than 30000 patients in a US electronic health record database[J]. Diabetes, Obesity and Metabolism, 2020, 22(4): 622-630.
[22] Gao C, Zhong H, Chen L, et al. Clinical and psychological assessment of patients with rheumatoid arthritis and fibromyalgia: A real-world study[J]. Journal of Clinical Rheumatology, 2022, 41(4): 1235-1240.
[23] Martinotti G, Vita A, Fagiolini A, et al. Real-world experience of esketamine use to manage treatment-resistant depression: A multicentric study on safety and effective⁃ness (REAL-ESK study) [J]. Journal of Affective Disorders, 2022, 319: 646-654.
[24] Quilichini J B, Revet A, Garcia P, et al. Comparative effects of 15 antidepressants on the risk of withdrawal syndrome: A real-world study using the WHO pharmacovigilance database[J]. Journal of Affective Disorders, 2022, 297: 189-193.
[25] Burdet C, Ader F. Real-world effectiveness of oral antivirals for COVID-19[J]. The Lancet, 2022, 400(10359): 1175-1176.
[26] Zheng C, Shao W, Chen X, et al. Real-world effectiveness of COVID-19 vaccines: A literature review and meta-analysis[J]. International Journal of Infectious Disorders, 2022, 114: 252-260.
[27] Kostka K, Duarte-Salles T, Prats-Uribe A, et al. Unraveling COVID-19: A large-scale characterization of 4.5 million COVID-19 cases using CHARYBDIS[J]. Journal of Clinical Epidemiology, 2022, 14: 369-384.
[28] Chen Q, Wang L, Li C, et al. Chronic cardio-metabolic disease increases the risk of worse outcomes among hospitalized patients With COVID-19: A multicenter, retrospective, and real-world study[J]. Journal of the American Heart Association, 2021, 10(12): e018451.
[29] Tavazzi L. Clinical research methodology process: What has changed with COVID-19[J]. European Heart Journal, 2022, 24(Suppl): I175-I180.
[30] 中华中医药学会《中医药真实世界研究技术规范》制订组. 中医药真实世界研究技术规范——证据质量评价与报告[J]. 中医杂志, 2022, 63(3): 293-300.
[31] Morin E. From the concept of system to the paradigm of complexity[J]. Journal of Social and Evolutionary Systems, 1992, 15(4): 371-385.
[32] 董家鸿院士：我国医学研究要从知识驱动向临床问题驱动转变[EB/OL]. (2022-01-11)[2023-01-12]. https://www.cn-healthcare.com/article/20220111/content-564927.html.
[33] Kuhn T S. The structure of scientific revolutions[M]. Chicago: University of Chicago Press, 1970.
[34] 刘保延, 周雪忠, 张润顺, 等 . 个体诊疗临床科研信息一体化平台[J]. 中国数字医学, 2007, 2(6): 30-35.
[35] 刘保延. 数字中医药与中医药的跨越式发展[J]. 中国中医药信息杂志, 2002, 9(8): 1-2.
[36] 刘保延 . 真实世界的中医临床科研范式[J]. 中医杂志, 2013, 54(6): 451-455.
[37] 张超中. 中医范式的真实性问题——兼评〈真实世界的中医临床科研范式〉[J]. 中医杂志, 2013, 54(14): 1171-1174.
[38] 黄欣荣 . 复杂性科学与中医[J]. 中医杂志, 2013, 54(19): 1621-1626.
[39] 钟义信 . 人体研究的方法论挑战[J]. 中医杂志, 2014, 55(1): 2-7.
[40] 吴彤. 中西医诊疗实践中的身体、空间和技术——从身体观看中西医学模式的差异[J]. 中医杂志, 2013, 54(22): 1891-1895.
[41] 王思成, 刘保延, 熊宁宁, 等 . 真实世界临床研究伦理问题及策略探讨[J]. 中国中西医结合杂志, 2013, 33(4): 437-442.
[42] 张润顺, 王映辉, 刘保延, 等 . 基于共享系统的真实世界中医临床研究范式初步实施方案的设计[J]. 中医杂志, 2014, 55(18): 1551-1554.
[43] 刘保延, 谢琪, 史华新, 等 . 构建真实世界临床研究技术平台的组织管理策略[J]. 中医杂志, 2013, 54(24): 2071-2075.
[44] 刘保延. 中医临床疗效评价研究的现状与展望[J]. 中国科学基金, 2010(5): 268-273.
[45] 刘保延, 李平, 翁维良, 等. SARS肺部炎症演变规律及动态分布特征的探讨[J]. 北京中医药大学学报, 2004, 27(4): 68-71.
[46] Zhou X, Chen S, Liu B, et al. Development of traditional Chinese medicine clinical data warehouse for medical knowledge discovery and decision support[J]. Artificial Intelligence in Medicine, 2010, 48(2/3): 139-152.
[47] 周雪忠, 刘保延, 王映辉, 等 . 复方药物配伍的复杂网络方法研究[J]. 中国中医药信息杂志, 2008, 15(11): 98-100.
[48] 曹克刚, 於堃, 高颖. 缺血性中风急性期预后相关因素的多因素分析[J]. 天津中医药, 2007, 24(6): 462-464.
[49] 倪青, 陈世波, 周雪忠, 等. 2型糖尿病合并代谢综合征患者并发症特征分析[J]. 中医杂志, 2007(9): 809-811.
[50] 高铸烨, 徐浩, 史大卓, 等 . 基于关联规则挖掘对急性冠脉综合征遣药组方规律的分析[J]. 辽宁中医杂志, 2007, 34(3): 284-285.
[51] 王映辉, 姜在旸, 闫英杰, 等 . 基于信息和数据挖掘技术的名老中医临床诊疗经验研究思路[J]. 世界科学技术-中医药现代化, 2005, 5(1): 98-105.
[52] Yang K, Zhang R, He L, et al. Multistage analysis method for detection of effective herb prescription from clinical data[J]. Frontiers of Medicine, 2018, 12(2): 206-217.
[53] 省格丽, 刘晶晶, 于泽丛, 等 . 基于复杂网络的国医大师沈宝藩治疗冠心病用药规律分析[J]. 湖南中医药大学学报, 2021, 41(7): 986-991.
[54] 孔维莲, 徐丽丽, 薛燕星, 等. 基于复杂网络的薛伯寿教授临床处方用药规律分析研究[J]. 世界科学技术-中医药现代化, 2017, 19(1): 55-62.
[55] 李平, 周雪忠 . 基于无尺度网络模型的路志正教授核心经验方药初步探讨[J]. 中国中医药信息杂志, 2008, 15(8): 96-97.
[56] Chan K W, Wong V T, Tang S C W. COVID-19: An update on the epidemiological, clinical, preventive and therapeutic evidence and guidelines of integrative Chinese-Western medicine for the management of 2019 novel coronavirus disease[J]. The American Journal of Chinese Medicine, 2020, 48(3): 737-762.
[57] Jiang L, Liu B, Xie Q, et al. Investigation into the influence of physician for treatment based on syndrome differentiation[J]. Evidence-Based Complementary and Alternative Medicine, 2013, 2013: 587234.
[58] 孙亚男, 于长禾, 袁玉虎, 等 . 辨证论治失眠疗效评价医生队列研究[J]. 辽宁中医药大学学报, 2017,19(9): 73-77.
[59] 刘保延, 何丽云, 周雪忠, 等 . 辨证论治临床疗效评价的新思路, 新方法与新策略[J]. 中医杂志, 2020, 61(2): 93-97.
[60] Shu Z, Chang K, Zhou Y, et al. Add-on Chinese medicine for coronavirus disease 2019 (ACCORD): A retrospective cohort study of hospital registries[J]. The American Journal of Chinese Medicine, 2021, 49(3):543-575.
[61] Liu B, Zhou X, Wang Y, et al. Data processing and analysis in real-world traditional Chinese medicine clinical data: Challenges and approaches[J]. Statistics in Medicine, 2012, 31(7): 653-660.
[62] Hernán M A, Wang W, Leaf D E. Target trial emulation: A framework for causal inference from observational data[J]. Journal of The American Medical Association, 2022, 328(24): 2446-2447.
[63] Zhang R, Wang Y, Liu B, et al. Clinical data qualityproblems and countermeasure for real world study[J].Frontiers of Medicine, 2014, 8(3): 352357.
[64] Hripcsak G, Duke J D, Shah N H, et al. Observational health data sciences and informatics (OHDSI): Opportunities for observational researchers[J]. Studies in Health Technology and Informatics, 2015, 216: 574-578.
[65] Fleurence R L, Curtis L H, Califf R M, et al. Launching PCORnet, a national patient-centered clinical research network[J]. Journal of the American Medical Informatics Association, 2014, 21(4): 578-82.
[66] Gallacher J, de Reydet de Vulpillieres F, Amzal B, et al. Challenges for optimizing real-world evidence in alzheimer's disease: The ROADMAP project[J]. Journal of Alzheimer's Disease, 2019, 67(2): 495-501.
[67] Lam S S W, Fang A H S, Koh M S, et al. Development of a real-world database for asthma and COPD: The singhealth-duke-nus-gSK COPD and asthma real-world evidence (SDG-CARE) collaboration[J]. BMC Medical Informatics and Decision Making, 2023, 23(1): 4.
[68] Zou Q, Yang K, Shu Z, et al. Phenonizer: A fine-grained phenotypic named entity recognizer for Chinese clinical texts[J]. BioMed Research International, 2022, 2022: 3524090.
[69] Xu N, Zhong K, Yu H, et al. Add-on Chinese medicine for hospitalized chronic obstructive pulmonary disease (CHOP): A cohort study of hospital registry[J]. Phytomedicine, 2023, 109: 154586.
[70] Wang Y F, Wang J J, Peng W, et al. Identification of hypertension subgroups through topological analysis of symptom-based patient similarity[J]. Chinese Journal of Integrative Medicine, 2021, 27(9): 656-665.
[71] Shu Z, Liu W, Wu H, et al. Symptom-based network classification identifies distinct clinical subgroups of liver diseases with common molecular pathways[J]. Computer Methods and Programs in Biomedicine, 2019, 174: 41-50.
[72] Miksad R A, Abernethy A P. Harnessing the power of real-world evidence (RWE): A checklist to ensure regulatory-grade data quality[J]. Clinical Pharmacology & Therapeutics, 2018, 103(2): 202-205.
[73] Nelson C A, Butte A J, Baranzini S E. Integrating biomedical research and electronic health records to create knowledge-based biologically meaningful machine-readable embeddings[J]. Nature Communications, 2019, 10(1): 30-45.
[74] 张艳宏, 刘保延, 谢琪, 等 . 结局管理及其在真实世界中医临床研究中的应用[J]. 中医杂志, 2014, 55(11): 901-904.
[75] McDonald L, Lambrelli D, Wasiak R, et al. Real-world data in the United Kingdom: Opportunities and challenges[J]. BMC Medicine, 2016, 14(1): 97.
[76] Cave A, Kurz X, Arlett P. Real-world data for regulatory decision making: Challenges and possible solutions for Europe[J]. Clinical Pharmacology & Therapeutics, 2019, 106(1): 36-39.
[77] Rudrapatna V A, Butte A J. Opportunities and challenges in using real-world data for health care[J]. Journal of Clinical Investigation, 2020, 130(2): 565-574.
[78] Zhou X, Menche J, Barabási A L, et al. Human symptoms-disease network[J]. Nature Communications, 2014, 5: 4212.
[79] Zhou X, Lei L, Liu J, et al. A systems approach to refine disease taxonomy by integrating phenotypic and molecular networks[J]. eBioMedicine, 2018, 31: 79-91.
[80] Huang E W, Wang S, Li B, et al. HEMnet: Integration of electronic medical records with molecular interaction networks and domain knowledge for survival analysis[C]//Proceedings of the 8th ACM International Conference on Bioinformatics, Computational Biology, and Health Informatics, Boston: ACM, 2017: 378-387.
[81] 许宁, 董鑫, 钟昆禹, 等 . 基于复杂网络的特发性肺纤维化病证结合人群分型研究[J]. 世界科学技术-中医药现代化, 2021, 23(9): 3109-3117.
[82] Parikh R B, Helmchen L A. Paying for artificial intelligence in medicine[J]. npj Digital Medicine, 2022, 5(1): 63.
[83] Iregbu K, Dramowski A, Milton R, et al. Global health systems' data science approach for precision diagnosis of sepsis in early life[J]. The Lancet Infectious Diseases, 2022, 22(5): 143-152.
[84] Shickel B, Tighe P J, Bihorac A, et al. Deep EHR: A survey of recent advances in deep learning techniques for electronic health record (EHR) analysis[J]. IEEE Journal of Biomedical and Health Informatics, 2018, 22(5): 1589-1604.
[85] Liu R, Rizzo S, Whipple S, et al. Evaluating eligibility criteria of oncology trials using real-world data and AI[J]. Nature, 2021, 592(7855): 629-633.
[86] Pearl J. Causal inference in statistics: An overview[J]. Statistics Surveys, 2009, 3: 96-146.
