Open AccessSimilarity‐Based Modeling Applied to Signal Detection in Pharmacovigilance
Author(s) -
Vilar S,
Ryan PB,
Madigan D,
Stang PE,
Schuemie MJ,
Friedman C,
Tatonetti NP,
Hripcsak G
Publication year - 2014
Publication title -
cpt: pharmacometrics and systems pharmacology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.53
H-Index - 37
ISSN - 2163-8306
DOI - 10.1038/psp.2014.35
Subject(s) - pharmacovigilance , similarity (geometry) , computer science , signal (programming language) , data mining , artificial intelligence , pattern recognition (psychology) , medicine , pharmacology , drug , image (mathematics) , programming language
One of the main objectives in pharmacovigilance is the detection of adverse drug events (ADEs) through mining of healthcare databases, such as electronic health records or administrative claims data. Although different approaches have been shown to be of great value, research is still focusing on the enhancement of signal detection to gain efficiency in further assessment and follow‐up. We applied similarity‐based modeling techniques, using 2D and 3D molecular structure, ADE, target, and ATC (anatomical therapeutic chemical) similarity measures, to the candidate associations selected previously in a medication‐wide association study for four ADE outcomes. Our results showed an improvement in the precision when we ranked the subset of ADE candidates using similarity scorings. This method is simple, useful to strengthen or prioritize signals generated from healthcare databases, and facilitates ADE detection through the identification of the most similar drugs for which ADE information is available. CPT Pharmacometrics Syst. Pharmacol . (2014) 3, e137; doi: 10.1038/psp.2014.35 ; published online 24 September 2014