Investigating the detection of adverse drug events in a UK general practice electronic health-care database

Jenna Reps, Jan Feyereisl, Jonathan M. Garibaldi, Uwe Aickelin, Jack E. Gibson, Richard B. Hubbard

Research output: Contribution to conferencePaperpeer-review

1 Citation (Scopus)

Abstract

Data-mining techniques have frequently been developed for Spontaneous reporting databases. These techniques aim to find adverse drug events accurately and efficiently. Spontaneous reporting databases are prone to missing information, under reporting and incorrect entries. This often results in a detection lag or prevents the detection of some adverse drug events. These limitations do not occur in electronic healthcare databases. In this paper, existing methods developed for spontaneous reporting databases are implemented on both a spontaneous reporting database and a general practice electronic health-care database and compared. The results suggests that the application of existing methods to the general practice database may help find signals that have gone undetected when using the spontaneous reporting system database. In addition the general practice database provides far more supplementary information, that if incorporated in analysis could provide a wealth of information for identifying adverse events more accurately.

Original languageEnglish
Pages167-172
Number of pages6
Publication statusPublished - 2011
Externally publishedYes
Event11th UK Workshop on Computational Intelligence, UKCI 2011 - Manchester, United Kingdom
Duration: 7 Sep 20119 Sep 2011

Conference

Conference11th UK Workshop on Computational Intelligence, UKCI 2011
Country/TerritoryUnited Kingdom
CityManchester
Period7/09/119/09/11

ASJC Scopus subject areas

  • Computational Theory and Mathematics
  • Artificial Intelligence

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