Original Article
Application of data mining algorithm to detect adverse drug reaction signal
Jingyuan Zhang, Yuxia Bai, Sheng Han, Ligong Jiao, Xiaodong Guan, Luwen Shi
Published 2016-12-28
Cite as ADRJ, 2016, 18(6): 412-416. DOI: 10.3760/cma.j.issn.1008-5734.2016.06.003
Abstract
ObjectiveTo detect adverse drug reaction (ADR) signals using data mining algorithm and explore its application value.
MethodsReports on adverse reactions induced by anti-infective drugs in National centor for ADR monitoring from January 2009 to December 2013 were collected and potential ADR risk signals were detected using proportional reporting ratio method (PRR), reporting odds ratio method (ROR), Medicines and Healthcare Products Regulatory Agency method (MHRA), Bayesian confidence propagation neural network method (BCPNN), and multi-item gamma Poisson shrinker method (MGPS). The results of detection using the above-mentioned 5 signal detection methods were compared.
ResultsA total of 35 807 ADR reports induced by anti-infective drugs were collected, 35 759 effective reports were entered, and 834 suspected drugs were involved. In the 35 759 reports, 464 kinds of ADR were defined according to lowest level term and 21 kinds of ADR were defined according to system/organ classification. After the data cleaning, splitting, and encoding process, 6 620 reports containing suspected drug-adverse reaction combination were acquired. There were 3 966 reports (59.91%) in which suspected drug-adverse reaction combination appeared once, 937 reports (14.15%) in which suspected drug-adverse reaction combination appeared twice, and 1 717 reports (25.94%) in which suspicious drug-adverse reaction combination appeared more than thrice. The number of ADR signals detected using PRR, ROR, MGPS, BCPNN, and MHRA was 651, 614, 306, 75, and 57, respectively; the categories of drugs were 194, 168, 124, 34 and 40, respectively; ADR types were 139, 139, 121, 35, and 40, respectively. In the top ten risk signals, azithromycin-nausea were detected by the 5 signal detection methods, levofloxacin-pruritus were detected by PRR, ROR, MHRA, and BCPNN. The top ten signals detected by PRR were totally same as those by ROR and signals detected by other methods were various.
ConclusionsPotential risk signals in ADR reports could be detected systematically and automatically using PRR, ROR, MGPS, BCPNN, and MHRA. However, each method has its own advantage and disadvantage and should be applied according to the actual situation and demand.
Key words:
Data mining; Adverse drug reactions
Contributor Information
Jingyuan Zhang
School of Pharmaceutical Sciences, Peking University, Beijing 100191, China
Yuxia Bai
School of Pharmaceutical Sciences, Peking University, Beijing 100191, China
Sheng Han
International Research Center of Medicinal Administration, Peking University, Beijing 100191, China
Ligong Jiao
Beijing Center for Adverse Drug Reactions Monitoring, Beijing 100054, China
Xiaodong Guan
School of Pharmaceutical Sciences, Peking University, Beijing 100191, China
International Research Center of Medicinal Administration, Peking University, Beijing 100191, China
Luwen Shi
School of Pharmaceutical Sciences, Peking University, Beijing 100191, China
International Research Center of Medicinal Administration, Peking University, Beijing 100191, China
