Principle of adverse drug reaction signal detection methods and their applications_Chinese Journal of Evidence-Based Medicine

Authors：

SHUAI Yuxing ¹ , XIE Hao ² , LI Zhiying ¹ , HE Zhenfan ³ , CHEN Zhuoqun ¹ , CHEN Yiguo ⁴ , HUANG Ye’en ¹ , MA Qing ¹ , LI Ziyuan ¹ ,  CHEN Xinlin ¹

1. School of Basic Medical Sciences, Guangzhou University of Chinese Medicine, Guangzhou 510006, P. R. China;
2. The Second Clinical Medical College, Guangzhou University of Chinese Medicine, Guangzhou 510403, P. R. China;
3. Shenzhen Clinical Medical College, Guangzhou University of Chinese Medicine, Shenzhen 518000, P. R. China;
4. School of Medical Information Engineering, Guangzhou University of Chinese Medicine, Guangzhou 510006, P. R. China;

Corresponding?author：

CHEN Xinlin, Email: chenxlsums@126.com

Keywords：

Adverse drug reaction; Signal detection; Classical detection method; Modern detection method

DOI：

10.7507/1672-2531.202601165

Video：

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Abstract Full text Figures/Tables Video References Cited by

Adverse drug reaction (ADR) signal detection serves as a core component of pharmacovigilance, with its methodological framework continuously enriched by advancements in data science and artificial intelligence. This article systematically reviews and elaborates on the principles, implementation pathways, and application scenarios of mainstream ADR signal detection methods, including traditional frequentist methods such as the proportional reporting ratio (PRR), reporting odds ratio (ROR), and the comprehensive standard method; Bayesian probabilistic models such as the Bayesian confidence propagation neural network (BCPNN) and the multi-item gamma-Poisson Shrinker (MGPS); as well as machine learning and data mining techniques such as association rules, random forest, and zero-inflated models. By systematically comparing the advantages, limitations, applicable conditions, and empirical studies of these methods in the monitoring of both traditional Chinese medicine and chemical drugs, this study aims to provide researchers and regulatory agencies with a comprehensive methodological reference framework to facilitate the selection, optimization, and integrated application of detection approaches. Moving forward, improving data quality and promoting multi-technology collaboration will be crucial directions for enhancing the sensitivity and specificity of ADR signal detection, thereby supporting more precise decision-making in clinical medication safety.

Citation： SHUAI Yuxing, XIE Hao, LI Zhiying, HE Zhenfan, CHEN Zhuoqun, CHEN Yiguo, HUANG Ye’en, MA Qing, LI Ziyuan, CHEN Xinlin. Principle of adverse drug reaction signal detection methods and their applications. Chinese Journal of Evidence-Based Medicine, 2026, 26(9): 1081-1087. doi: 10.7507/1672-2531.202601165 Copy

1.	李苑雅, 張艷, 沈愛宗. 基于自發呈報系統藥品不良反應信號檢測方法的研究進展[J]. 安徽醫藥, 2015, (7): 1233-1236.
2.	Jia P, Zhou Y, Gao Y, et al. Real-world pharmacovigilance investigation of imipenem/cilastatin: signal detection using the FDA Adverse Event Reporting System (FAERS) database[J]. Front Pharmacol, 2025, 16: 1524159.
3.	Hauben M, Zhou X. Quantitative methods in pharmacovigilance: focus on signal detection[J]. Drug Saf, 2003, 26(3): 159-186.
4.	羅寶章, 錢軼峰, 葉小飛, 等. 藥物不良反應信號檢測方法的現狀與展望[J]. 藥學服務與研究, 2009, 9(4): 255-260.
5.	江靜, 侯永芳, 劉秀娟, 等. 藥品不良反應信號檢測方法概述[J]. 中國藥物警戒, 2010, 7(2): 78-80.
6.	Mizuno T, Sakai T, Tanabe K, et al. Visualization of kinase inhibition-related adverse events using the Japanese adverse drug event report database[J]. Drugs Real World Outcomes, 2021, 8(2): 197-206.
7.	李蓉, 董志, 路曉欽, 等. 熱毒寧注射液不良反應/事件監測及安全性評價[J]. 中國新藥與臨床雜志, 2014, 33(3): 233-236.
8.	Grundmark B, Holmberg L, Garmo H, et al. Reducing the noise in signal detection of adverse drug reactions by standardizing the background: a pilot study on analyses of proportional reporting ratios-by-therapeutic area[J]. Eur J Clin Pharmacol, 2014, 70(5): 627-635.
9.	汪海孫, 居靖, 黃萍, 等. 報告率比例法在中藥注射劑不良反應/事件信號提取中的應用[J]. 中國藥物警戒, 2009, 6(9): 530-533.
10.	Guan Y, Ji L, Zheng L, et al. Development of a drug risk analysis and assessment system and its application in signal excavation and analysis of 263 cases of fluoroquinolone-induced adverse reactions[J]. Front Pharmacol, 2022, 13: 892503.
11.	鄒元平, 林宇, 邢珂, 等. 中藥不良反應信號檢測系統的建立[J]. 中國藥房, 2012, 23(27): 2584-2587.
12.	魏志軍, 程能能, 何樂, 等. 上海市藥品不良反應自發呈報數據庫定量信號檢測系統的建立[J]. 復旦學報(醫學版), 2006, 33(4): 475-478,490.
13.	Huang L, Zheng D, Zalkikar J, et al. Zero-inflated Poisson model based likelihood ratio test for drug safety signal detection[J]. Stat Methods Med Res, 2017, 26(1): 471-488.
14.	Zhai Y, Ye X, Hu F, et al. Endocrine toxicity of immune checkpoint inhibitors: a real-world study leveraging US Food and Drug Administration adverse events reporting system[J]. J Immunother Cancer, 2019, 7(1): 286.
15.	崔盈盈, 吳嘉瑞, 譚迪, 等. 基于關聯規則的清開靈注射液致藥品不良反應發生規律的研究[J]. 中國醫院用藥評價與分析, 2017, 17(12): 1585-1590.
16.	廖星, 于丹丹, 謝雁鳴, 等. 碟脈靈苦碟子注射液上市后安全性監測: 一項30233例真實世界注冊登記研究[J]. 中國中藥雜志, 2017, 42(15): 2857-2863.
17.	Wiyono S, Abidin T. Comparative study of machine learning KNN, SVM, and decision tree algorithm to predict student’s performance[J]. Int J Comput Sci Mat, 2020, 6(2): 50-53.
18.	吉萌萌. 清開靈注射液醫院集中監測不良反應影響因素研究[D]. 北京: 中國中醫科學院, 2020.
19.	錢維. 藥品不良反應監測中隨機森林方法的建立與實現[D]. 上海: 第二軍醫大學, 2012.
20.	Hamada K, Abe T, Kouda K, et al. Estimating culprit drugs for adverse drug reactions based on Bayesian inference[J]. Clin Pharmacol Ther, 2023, 113(5): 1117-1124.
21.	陳文戈, 李嬋娟, 江靜, 等. 基于BCPNN法的藥品不良反應信號檢測與自動預警技術研究[J]. 計算機應用研究, 2009, 26(4): 1394-1397.
22.	Bate A, Lindquist M, Orre R, et al. Data-mining analyses of pharmacovigilance signals in relation to relevant comparison drugs[J]. Eur J Clin Pharmacol, 2002, 58(7): 483-490.
23.	張雪, 談儀炯, 熊學惠, 等. 基于FDA不良事件報告系統數據庫的阿柏西普眼內注射溶液不良反應信號挖掘[J]. 臨床藥物治療雜志, 2024, 22(1): 65-70.
24.	Ji X, Wang L, Hua L, et al. Improved adverse drug event prediction through information component guided pharmacological network model (IC-PNM)[J]. IEEE/ACM Trans Comput Biol Bioinform, 2021, 18(3): 1113-1121.
25.	劉靖, 葉國菊, 王啟明, 等. 模糊貝葉斯置信度遞進神經網絡法檢測藥品不良反應報告信號[J]. 中國藥物警戒, 2022, 19(10): 1113-1117.
26.	Ding Y, Markatou M, Ball R. An evaluation of statistical approaches to postmarketing surveillance[J]. Stat Med, 2020, 39(7): 845-874.
27.	Dumouchel W, Pregibon D. Empirical bayes screening for multi-item associations[C]//Proceedings of the Seventh ACM Sigkdd International Conference on Knowledge Discovery and Data Mining, 2001.
28.	Wilson AM, Thabane L, Holbrook A. Application of data mining techniques in pharmacovigilance[J]. Br J Clin Pharmacol, 2004, 57(2): 127-134.
29.	Denti F, Guindani M, Leisen F, et al. Two-group Poisson-Dirichlet mixtures for multiple testing[J]. Biometrics, 2021, 77(2): 622-633.
30.	吳學福, 劉振球, 吳明山, 等. 零膨脹計數資料幾種模型方法的比較研究[J]. 中國衛生統計, 2020, 37(3): 331-334.
31.	Weng J, Yang D, Qian T, et al. Combining zero-inflated negative binomial regression with MLRT techniques: an approach to evaluating shipping accident casualties[J]. Ocean Engineering, 2018, 166: 135-144.
32.	Chhabra P, Chen X, Weiss SR. Adverse event reporting patterns of newly approved drugs in the USA in 2006: an analysis of FDA Adverse Event Reporting System data[J]. Drug Saf, 2013, 36(11): 1117-1123.
33.	王盼. 基線模型在清開靈注射液與抗生素聯合用藥ADR信號檢測中的應用研究[D]. 北京: 北京中醫藥大學, 2012.
34.	程光文, 曾婧, 毛宗福. 雙黃連注射劑不良反應影響因素的logistic回歸分析[J]. 中國現代應用藥學, 2010, 27(10): 948-949.
35.	洪小鳳, 林琴, 陳瀟瀟, 等. 血必凈注射液不良反應影響因素的Logistic回歸分析[J]. 中國醫院用藥評價與分析, 2013, 13(10): 941-943.
36.	郭洪云. 中藥注射劑不良反應相關因素的Logistic分析[J]. 中國現代藥物應用, 2017, 11(2): 14-16.
37.	陳永平, 郝瑞. 影響喜炎平注射液在兒科給藥方式的多因素logistic分析[J]. 天津藥學, 2016, 28(4): 38-40.
38.	Arnau J, Bono R, Bendayan R, et al. Analyzing longitudinal data and use of the generalized linear model in health and social sciences[J]. Qual Quant, 2016, 50(2): 693-707.
39.	Cook AJ, Wellman RD, Marsh T, et al. Applying sequential surveillance methods that use regression adjustment or weighting to control confounding in a multisite, rare-event, distributed setting: part 2 in-depth example of a reanalysis of the measles-mumps-rubella-varicella combination vaccine and seizure risk[J]. J Clin Epidemiol, 2019, 113: 114-122.
40.	Eluwa GI, Badru T, Agu KA, et al. Adverse drug reactions to antiretroviral therapy (ARVs): incidence, type and risk factors in Nigeria[J]. BMC Clin Pharmacol, 2012, 12: 7.

1. 李苑雅, 張艷, 沈愛宗. 基于自發呈報系統藥品不良反應信號檢測方法的研究進展[J]. 安徽醫藥, 2015, (7): 1233-1236.
2. Jia P, Zhou Y, Gao Y, et al. Real-world pharmacovigilance investigation of imipenem/cilastatin: signal detection using the FDA Adverse Event Reporting System (FAERS) database[J]. Front Pharmacol, 2025, 16: 1524159.
3. Hauben M, Zhou X. Quantitative methods in pharmacovigilance: focus on signal detection[J]. Drug Saf, 2003, 26(3): 159-186.
4. 羅寶章, 錢軼峰, 葉小飛, 等. 藥物不良反應信號檢測方法的現狀與展望[J]. 藥學服務與研究, 2009, 9(4): 255-260.
5. 江靜, 侯永芳, 劉秀娟, 等. 藥品不良反應信號檢測方法概述[J]. 中國藥物警戒, 2010, 7(2): 78-80.
6. Mizuno T, Sakai T, Tanabe K, et al. Visualization of kinase inhibition-related adverse events using the Japanese adverse drug event report database[J]. Drugs Real World Outcomes, 2021, 8(2): 197-206.
7. 李蓉, 董志, 路曉欽, 等. 熱毒寧注射液不良反應/事件監測及安全性評價[J]. 中國新藥與臨床雜志, 2014, 33(3): 233-236.
8. Grundmark B, Holmberg L, Garmo H, et al. Reducing the noise in signal detection of adverse drug reactions by standardizing the background: a pilot study on analyses of proportional reporting ratios-by-therapeutic area[J]. Eur J Clin Pharmacol, 2014, 70(5): 627-635.
9. 汪海孫, 居靖, 黃萍, 等. 報告率比例法在中藥注射劑不良反應/事件信號提取中的應用[J]. 中國藥物警戒, 2009, 6(9): 530-533.
10. Guan Y, Ji L, Zheng L, et al. Development of a drug risk analysis and assessment system and its application in signal excavation and analysis of 263 cases of fluoroquinolone-induced adverse reactions[J]. Front Pharmacol, 2022, 13: 892503.
11. 鄒元平, 林宇, 邢珂, 等. 中藥不良反應信號檢測系統的建立[J]. 中國藥房, 2012, 23(27): 2584-2587.
12. 魏志軍, 程能能, 何樂, 等. 上海市藥品不良反應自發呈報數據庫定量信號檢測系統的建立[J]. 復旦學報(醫學版), 2006, 33(4): 475-478,490.
13. Huang L, Zheng D, Zalkikar J, et al. Zero-inflated Poisson model based likelihood ratio test for drug safety signal detection[J]. Stat Methods Med Res, 2017, 26(1): 471-488.
14. Zhai Y, Ye X, Hu F, et al. Endocrine toxicity of immune checkpoint inhibitors: a real-world study leveraging US Food and Drug Administration adverse events reporting system[J]. J Immunother Cancer, 2019, 7(1): 286.
15. 崔盈盈, 吳嘉瑞, 譚迪, 等. 基于關聯規則的清開靈注射液致藥品不良反應發生規律的研究[J]. 中國醫院用藥評價與分析, 2017, 17(12): 1585-1590.
16. 廖星, 于丹丹, 謝雁鳴, 等. 碟脈靈苦碟子注射液上市后安全性監測: 一項30233例真實世界注冊登記研究[J]. 中國中藥雜志, 2017, 42(15): 2857-2863.
17. Wiyono S, Abidin T. Comparative study of machine learning KNN, SVM, and decision tree algorithm to predict student’s performance[J]. Int J Comput Sci Mat, 2020, 6(2): 50-53.
18. 吉萌萌. 清開靈注射液醫院集中監測不良反應影響因素研究[D]. 北京: 中國中醫科學院, 2020.
19. 錢維. 藥品不良反應監測中隨機森林方法的建立與實現[D]. 上海: 第二軍醫大學, 2012.
20. Hamada K, Abe T, Kouda K, et al. Estimating culprit drugs for adverse drug reactions based on Bayesian inference[J]. Clin Pharmacol Ther, 2023, 113(5): 1117-1124.
21. 陳文戈, 李嬋娟, 江靜, 等. 基于BCPNN法的藥品不良反應信號檢測與自動預警技術研究[J]. 計算機應用研究, 2009, 26(4): 1394-1397.
22. Bate A, Lindquist M, Orre R, et al. Data-mining analyses of pharmacovigilance signals in relation to relevant comparison drugs[J]. Eur J Clin Pharmacol, 2002, 58(7): 483-490.
23. 張雪, 談儀炯, 熊學惠, 等. 基于FDA不良事件報告系統數據庫的阿柏西普眼內注射溶液不良反應信號挖掘[J]. 臨床藥物治療雜志, 2024, 22(1): 65-70.
24. Ji X, Wang L, Hua L, et al. Improved adverse drug event prediction through information component guided pharmacological network model (IC-PNM)[J]. IEEE/ACM Trans Comput Biol Bioinform, 2021, 18(3): 1113-1121.
25. 劉靖, 葉國菊, 王啟明, 等. 模糊貝葉斯置信度遞進神經網絡法檢測藥品不良反應報告信號[J]. 中國藥物警戒, 2022, 19(10): 1113-1117.
26. Ding Y, Markatou M, Ball R. An evaluation of statistical approaches to postmarketing surveillance[J]. Stat Med, 2020, 39(7): 845-874.
27. Dumouchel W, Pregibon D. Empirical bayes screening for multi-item associations[C]//Proceedings of the Seventh ACM Sigkdd International Conference on Knowledge Discovery and Data Mining, 2001.
28. Wilson AM, Thabane L, Holbrook A. Application of data mining techniques in pharmacovigilance[J]. Br J Clin Pharmacol, 2004, 57(2): 127-134.
29. Denti F, Guindani M, Leisen F, et al. Two-group Poisson-Dirichlet mixtures for multiple testing[J]. Biometrics, 2021, 77(2): 622-633.
30. 吳學福, 劉振球, 吳明山, 等. 零膨脹計數資料幾種模型方法的比較研究[J]. 中國衛生統計, 2020, 37(3): 331-334.
31. Weng J, Yang D, Qian T, et al. Combining zero-inflated negative binomial regression with MLRT techniques: an approach to evaluating shipping accident casualties[J]. Ocean Engineering, 2018, 166: 135-144.
32. Chhabra P, Chen X, Weiss SR. Adverse event reporting patterns of newly approved drugs in the USA in 2006: an analysis of FDA Adverse Event Reporting System data[J]. Drug Saf, 2013, 36(11): 1117-1123.
33. 王盼. 基線模型在清開靈注射液與抗生素聯合用藥ADR信號檢測中的應用研究[D]. 北京: 北京中醫藥大學, 2012.
34. 程光文, 曾婧, 毛宗福. 雙黃連注射劑不良反應影響因素的logistic回歸分析[J]. 中國現代應用藥學, 2010, 27(10): 948-949.
35. 洪小鳳, 林琴, 陳瀟瀟, 等. 血必凈注射液不良反應影響因素的Logistic回歸分析[J]. 中國醫院用藥評價與分析, 2013, 13(10): 941-943.
36. 郭洪云. 中藥注射劑不良反應相關因素的Logistic分析[J]. 中國現代藥物應用, 2017, 11(2): 14-16.
37. 陳永平, 郝瑞. 影響喜炎平注射液在兒科給藥方式的多因素logistic分析[J]. 天津藥學, 2016, 28(4): 38-40.
38. Arnau J, Bono R, Bendayan R, et al. Analyzing longitudinal data and use of the generalized linear model in health and social sciences[J]. Qual Quant, 2016, 50(2): 693-707.
39. Cook AJ, Wellman RD, Marsh T, et al. Applying sequential surveillance methods that use regression adjustment or weighting to control confounding in a multisite, rare-event, distributed setting: part 2 in-depth example of a reanalysis of the measles-mumps-rubella-varicella combination vaccine and seizure risk[J]. J Clin Epidemiol, 2019, 113: 114-122.
40. Eluwa GI, Badru T, Agu KA, et al. Adverse drug reactions to antiretroviral therapy (ARVs): incidence, type and risk factors in Nigeria[J]. BMC Clin Pharmacol, 2012, 12: 7.

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