title: High-Performance Signal Detection for Adverse Drug Events using MapReduce Paradigm.
abstract: Post-marketing pharmacovigilance is important for public health, as many Adverse Drug Events (ADEs) are unknown when those drugs were approved for marketing. However, due to the large number of reported drugs and drug combinations, detecting ADE signals by mining these reports is becoming a challenging task in terms of computational complexity. Recently, a parallel programming model, MapReduce has been introduced by Google to support large-scale data intensive applications. In this study, we proposed a MapReduce-based algorithm, for common ADE detection approach, Proportional Reporting Ratio (PRR), and tested it in mining spontaneous ADE reports from FDA. The purpose is to investigate the possibility of using MapReduce principle to speed up biomedical data mining tasks using this pharmacovigilance case as one specific example. The results demonstrated that MapReduce programming model could improve the performance of common signal detection algorithm for pharmacovigilance in a distributed computation environment at approximately liner speedup rates.
authors.affiliation: Institute of Network Computing and Information System of Peking University, China.
authors.forename: Kai
authors.initials: K
authors.lastname: Fan
authors.forename: Xingzhi
authors.initials: X
authors.lastname: Sun
authors.forename: Ying
authors.initials: Y
authors.lastname: Tao
authors.forename: Linhao
authors.initials: L
authors.lastname: Xu
authors.forename: Chen
authors.initials: C
authors.lastname: Wang
authors.forename: Xianling
authors.initials: X
authors.lastname: Mao
authors.forename: Bo
authors.initials: B
authors.lastname: Peng
authors.forename: Yue
authors.initials: Y
authors.lastname: Pan
mesh_terms: D016907:Adverse Drug Reaction Reporting Systems; D000465:Algorithms; D057225:Data Mining; D016208:Databases, Factual; D064420:Drug-Related Side Effects and Adverse Reactions; D006801:Humans; D060735:Pharmacovigilance