Text and Data Mining Techniques in Adverse Drug Reaction Detection

被引:88
作者
Karimi, Sarvnaz [1 ]
Wang, Chen [1 ]
Metke-Jimenez, Alejandro [2 ]
Gaire, Raj [3 ]
Paris, Cecile [1 ]
机构
[1] CSIRO, Sydney, NSW, Australia
[2] CSIRO, Australian eHlth Res Ctr, Brisbane, Qld, Australia
[3] CSIRO, Canberra, ACT, Australia
关键词
Algorithms; Performance; Drug side effect; adverse drug reaction; drug safety; data mining; text mining; statistical analysis; signal detection; ELECTRONIC HEALTH RECORDS; COMPUTERIZED SURVEILLANCE; MEDICATION INFORMATION; SAFETY SURVEILLANCE; REACTION SIGNALS; SOCIAL NETWORKS; EVENTS; ASSOCIATIONS; DESIGN; SYSTEM;
D O I
10.1145/2719920
中图分类号
TP301 [理论、方法];
学科分类号
081202 ;
摘要
We review data mining and related computer science techniques that have been studied in the area of drug safety to identify signals of adverse drug reactions from different data sources, such as spontaneous reporting databases, electronic health records, and medical literature. Development of such techniques has become more crucial for public heath, especially with the growth of data repositories that include either reports of adverse drug reactions, which require fast processing for discovering signals of adverse reactions, or data sources that may contain such signals but require data or text mining techniques to discover them. In order to highlight the importance of contributions made by computer scientists in this area so far, we categorize and review the existing approaches, and most importantly, we identify areas where more research should be undertaken.
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页数:39
相关论文
共 143 条
  • [1] Agresti A., 2007, An introduction to categorical data analysis, V423
  • [2] Alghabban A., 2004, Dictionary of pharmacovigilance
  • [3] Correlation versus Causation? Pharmacovigilance of the Analgesic Flupirtine Exemplifies the Need for Refined Spontaneous ADR Reporting
    Anderson, Nora
    Borlak, Juergen
    [J]. PLOS ONE, 2011, 6 (10):
  • [4] [Anonymous], 2011, 1 INT WORKSH KNOWL
  • [5] [Anonymous], 2002, 21 ACM SIGACT SIGMOD, DOI DOI 10.1145/543613.543644
  • [6] [Anonymous], 2011, National Safety and Quality Health Service Standards
  • [7] [Anonymous], 2002, P 8 ACM SIGKDD INT C, DOI DOI 10.1145/775047.775087
  • [8] Aronson AR, 2001, J AM MED INFORM ASSN, P17
  • [9] An Algorithmic Framework for Predicting Side Effects of Drugs
    Atias, Nir
    Sharan, Roded
    [J]. JOURNAL OF COMPUTATIONAL BIOLOGY, 2011, 18 (03) : 207 - 218
  • [10] Design and validation of an automated method to detect known adverse drug reactions in MEDLINE: a contribution from the EU-ADR project
    Avillach, Paul
    Dufour, Jean-Charles
    Diallo, Gayo
    Salvo, Francesco
    Joubert, Michel
    Thiessard, Frantz
    Mougin, Fleur
    Trifiro, Gianluca
    Fourrier-Reglat, Annie
    Pariente, Antoine
    Fieschi, Marius
    [J]. JOURNAL OF THE AMERICAN MEDICAL INFORMATICS ASSOCIATION, 2013, 20 (03) : 446 - 452