Comparing data-mining algorithms developed for longitudinal observational databases

Jenna Reps, Jonathan M. Garibaldi, Uwe Aickelin, Daniele Soria, Jack E. Gibson, Richard B. Hubbard

Research output: Chapter in Book/Conference proceedingConference contributionpeer-review

4 Citations (Scopus)

Abstract

Longitudinal observational databases have become a recent interest in the post marketing drug surveillance community due to their ability of presenting a new perspective for detecting negative side effects. Algorithms mining longitudinal observation databases are not restricted by many of the limitations associated with the more conventional methods that have been developed for spontaneous reporting system databases. In this paper we investigate the robustness of four recently developed algorithms that mine longitudinal observational databases by applying them to The Health Improvement Network (THIN) for six drugs with well document known negative side effects. Our results show that none of the existing algorithms was able to consistently identify known adverse drug reactions above events related to the cause of the drug and no algorithm was superior.

Original languageEnglish
Title of host publication2012 12th UK Workshop on Computational Intelligence, UKCI 2012
DOIs
Publication statusPublished - 2012
Externally publishedYes
Event2012 12th UK Workshop on Computational Intelligence, UKCI 2012 - Edinburgh, United Kingdom
Duration: 5 Sep 20127 Sep 2012

Publication series

Name2012 12th UK Workshop on Computational Intelligence, UKCI 2012

Conference

Conference2012 12th UK Workshop on Computational Intelligence, UKCI 2012
Country/TerritoryUnited Kingdom
CityEdinburgh
Period5/09/127/09/12

ASJC Scopus subject areas

  • Artificial Intelligence
  • Computational Theory and Mathematics

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