title: A functional temporal association mining approach for screening potential drug-drug interactions from electronic patient databases.
abstract: OBJECTIVE
  Drug-drug interactions (DDIs) can result in serious consequences, including death. Existing methods for identifying potential DDIs in post-marketing surveillance primarily rely on spontaneous reports. These methods suffer from severe underreporting, incompleteness, and various bias. The aim of this study was to more effectively screen potential DDIs using patient electronic data and temporal association mining techniques.
  
  
  METHODS
  We focus on discovery of potential DDIs by analyzing the temporal relationships between the concurrent use of two drugs of interest and the occurrences of various symptoms. We introduced innovative functional temporal association rules where the degree of temporal association between two events within a patient case was defined by a function.
  
  
  RESULTS
  Preliminary test results on two drug pairs (i.e., <benazepril, losartan> and <benazepril, insulin>) were classified into 260 clinically meaningful categories. These categories were evaluated by physicians and the results exhibited that all the potential DDIs were confined to top 20 of the 260 outcomes.
  
  
  CONCLUSIONS
  Our methodology can be used to dramatically reduce a long list of association rules to a manageable list for further analysis and investigation by drug safety professionals.
authors.affiliation: a Department of Electrical and Computer Engineering , Gonzaga University , Spokane , WA , USA.
authors.forename: Yanqing
authors.initials: Y
authors.lastname: Ji
authors.affiliation: b Department of Electrical and Computer Engineering , Wayne State University , Detroit , MI , USA.
authors.forename: Hao
authors.initials: H
authors.lastname: Ying
authors.affiliation: c Frontier Behavioral Health , Spokane , WA , USA.
authors.forename: John
authors.initials: J
authors.lastname: Tran
authors.affiliation: d Department of Medicine , St. Mary Mercy Hospital (Trinity Health) , Livonia , MI , USA.
authors.forename: Peter
authors.initials: P
authors.lastname: Dews
authors.affiliation: e Department of Pharmacy, Albertsons , Spokane WA , USA.
authors.forename: See-Yan
authors.initials: SY
authors.lastname: Lau
authors.affiliation: f Research for the Critical Junctures Institute , Bellingham , WA , USA.
authors.forename: R Michael
authors.initials: RM
authors.lastname: Massanari
mesh_terms: D016208:Databases, Factual; D004347:Drug Interactions; D057286:Electronic Health Records; D006801:Humans
keywords: Data-mining algorithms; drug–drug interactions; interestingness measures; post-marketing surveillance; temporal association mining