These tools will no longer be maintained as of December 31, 2024. Archived website can be found here. PubMed4Hh GitHub repository can be found here. Contact NLM Customer Service if you have questions.
167 related articles for article (PubMed ID: 27225325)
21. Pharmacovigilance - The next chapter. Moore N; Berdaï D; Blin P; Droz C Therapie; 2019 Dec; 74(6):557-567. PubMed ID: 31623850 [TBL] [Abstract][Full Text] [Related]
22. Use of measures of disproportionality in pharmacovigilance: three Dutch examples. Egberts AC; Meyboom RH; van Puijenbroek EP Drug Saf; 2002; 25(6):453-8. PubMed ID: 12071783 [TBL] [Abstract][Full Text] [Related]
23. Data mining spontaneous adverse drug event reports for safety signals in Singapore - a comparison of three different disproportionality measures. Ang PS; Chen Z; Chan CL; Tai BC Expert Opin Drug Saf; 2016 May; 15(5):583-90. PubMed ID: 26996192 [TBL] [Abstract][Full Text] [Related]
24. Improved statistical signal detection in pharmacovigilance by combining multiple strength-of-evidence aspects in vigiRank. Caster O; Juhlin K; Watson S; Norén GN Drug Saf; 2014 Aug; 37(8):617-28. PubMed ID: 25052742 [TBL] [Abstract][Full Text] [Related]
25. Comparison of three methods (an updated logistic probabilistic method, the Naranjo and Liverpool algorithms) for the evaluation of routine pharmacovigilance case reports using consensual expert judgement as reference. Théophile H; André M; Miremont-Salamé G; Arimone Y; Bégaud B Drug Saf; 2013 Oct; 36(10):1033-44. PubMed ID: 23828659 [TBL] [Abstract][Full Text] [Related]
26. Application of biclustering algorithm in adverse drug reaction monitoring system of China. Zhu T; Zhang Y; Ye X; Hou Y; Liu J; Shi W; Xu J; Guo X; He J Pharmacoepidemiol Drug Saf; 2018 Nov; 27(11):1257-1264. PubMed ID: 30232823 [TBL] [Abstract][Full Text] [Related]
27. Automatic detection of adverse events to predict drug label changes using text and data mining techniques. Gurulingappa H; Toldo L; Rajput AM; Kors JA; Taweel A; Tayrouz Y Pharmacoepidemiol Drug Saf; 2013 Nov; 22(11):1189-94. PubMed ID: 23935003 [TBL] [Abstract][Full Text] [Related]
28. Use of screening algorithms and computer systems to efficiently signal higher-than-expected combinations of drugs and events in the US FDA's spontaneous reports database. Szarfman A; Machado SG; O'Neill RT Drug Saf; 2002; 25(6):381-92. PubMed ID: 12071774 [TBL] [Abstract][Full Text] [Related]
29. Potential use of data-mining algorithms for the detection of 'surprise' adverse drug reactions. Hauben M; Horn S; Reich L Drug Saf; 2007; 30(2):143-55. PubMed ID: 17253879 [TBL] [Abstract][Full Text] [Related]
30. Multinomial modeling and an evaluation of common data-mining algorithms for identifying signals of disproportionate reporting in pharmacovigilance databases. Johnson K; Guo C; Gosink M; Wang V; Hauben M Bioinformatics; 2012 Dec; 28(23):3123-30. PubMed ID: 23064001 [TBL] [Abstract][Full Text] [Related]
31. Signal Detection of Imipenem Compared to Other Drugs from Korea Adverse Event Reporting System Database. Park K; Soukavong M; Kim J; Kwon KE; Jin XM; Lee J; Yang BR; Park BJ Yonsei Med J; 2017 May; 58(3):564-569. PubMed ID: 28332362 [TBL] [Abstract][Full Text] [Related]
32. Signal Detection for Baclofen in Web Forums: A Preliminary Study. Karapetiantz P; Audeh B; Lillo-Le Louët A; Bousquet C Stud Health Technol Inform; 2018; 247():421-425. PubMed ID: 29677995 [TBL] [Abstract][Full Text] [Related]
33. An Alternative to Disproportionality: A Frequency-Based Method for Pharmacovigilance Data Mining. Jokinen JD; Lievano F; Scarazzini L; Truffa M Ther Innov Regul Sci; 2018 May; 52(3):294-299. PubMed ID: 29714535 [TBL] [Abstract][Full Text] [Related]
34. High-Performance Signal Detection for Adverse Drug Events using MapReduce Paradigm. Fan K; Sun X; Tao Y; Xu L; Wang C; Mao X; Peng B; Pan Y AMIA Annu Symp Proc; 2010 Nov; 2010():902-6. PubMed ID: 21347109 [TBL] [Abstract][Full Text] [Related]
35. An algorithm to detect unexpected increases in frequency of reports of adverse events in EudraVigilance. Pinheiro LC; Candore G; Zaccaria C; Slattery J; Arlett P Pharmacoepidemiol Drug Saf; 2018 Jan; 27(1):38-45. PubMed ID: 29143393 [TBL] [Abstract][Full Text] [Related]
36. Evaluation of statistical association measures for the automatic signal generation in pharmacovigilance. Roux E; Thiessard F; Fourrier A; Bégaud B; Tubert-Bitter P IEEE Trans Inf Technol Biomed; 2005 Dec; 9(4):518-27. PubMed ID: 16379369 [TBL] [Abstract][Full Text] [Related]
37. Surveillance of drugs that most frequently induce acute kidney injury: A pharmacovigilance approach. Hosohata K; Inada A; Oyama S; Furushima D; Yamada H; Iwanaga K J Clin Pharm Ther; 2019 Feb; 44(1):49-53. PubMed ID: 30014591 [TBL] [Abstract][Full Text] [Related]
38. Pilot evaluation of an automated method to decrease false-positive signals induced by co-prescriptions in spontaneous reporting databases. Avillach P; Salvo F; Thiessard F; Miremont-Salamé G; Fourrier-Reglat A; Haramburu F; Bégaud B; Moore N; Pariente A; Pharmacoepidemiol Drug Saf; 2014 Feb; 23(2):186-94. PubMed ID: 23670805 [TBL] [Abstract][Full Text] [Related]
39. Adverse drug reaction or innocent bystander? A systematic comparison of statistical discovery methods for spontaneous reporting systems. Dijkstra L; Garling M; Foraita R; Pigeot I Pharmacoepidemiol Drug Saf; 2020 Apr; 29(4):396-403. PubMed ID: 32092786 [TBL] [Abstract][Full Text] [Related]
40. Comparison of two drug safety signals in a pharmacovigilance data mining framework. Tubert-Bitter P; Bégaud B; Ahmed I Stat Methods Med Res; 2016 Apr; 25(2):615-29. PubMed ID: 23070598 [TBL] [Abstract][Full Text] [Related] [Previous] [Next] [New Search]