title: A temporal interestingness measure for drug interaction signal detection in post-marketing surveillance.
abstract: Drug-drug interactions (DDIs) can result in serious consequences, including death. Existing methods for identifying potential DDIs in post-marketing surveillance primarily rely on the FDA's (Food and Drug Administration) spontaneous reporting system. However, this system suffers from severe underreporting, which makes it difficult to timely collect enough valid cases for statistical analysis. In this paper, we study how to signal potential DDIs using patient electronic health data. Specifically, 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 using novel temporal association mining techniques we developed. A new interestingness measure called functional temporal interest was proposed to assess the degrees of temporal association between two drugs of interest and each symptom. The measure was employed to screen potential DDIs from 21,405 electronic patient cases retrieved from the Veterans Affairs Medical Center in Detroit, Michigan. The preliminary results indicate the usefulness of our method in finding potential DDIs for further analysis (e.g., epidemiology study) and investigation (e.g., case review) by drug safety professionals.
authors.forename: Yanqing
authors.initials: Y
authors.lastname: Ji
authors.forename: Hao
authors.initials: H
authors.lastname: Ying
authors.forename: John
authors.initials: J
authors.lastname: Tran
authors.forename: Peter
authors.initials: P
authors.lastname: Dews
authors.forename: Ayman
authors.initials: A
authors.lastname: Mansour
authors.forename: R Michael
authors.initials: RM
authors.lastname: Massanari
mesh_terms: D057225:Data Mining / Q000379:methods*; D004347:Drug Interactions; D057286:Electronic Health Records; D006801:Humans; D008824:Michigan; D011358:Product Surveillance, Postmarketing / Q000379:methods*