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. 2013 May 1;20(3):446-52.
doi: 10.1136/amiajnl-2012-001083. Epub 2012 Nov 29.

Design and validation of an automated method to detect known adverse drug reactions in MEDLINE: a contribution from the EU-ADR project

Paul Avillach  1 , Jean-Charles Dufour, Gayo Diallo, Francesco Salvo, Michel Joubert, Frantz Thiessard, Fleur Mougin, Gianluca Trifirò, Annie Fourrier-Réglat, Antoine Pariente, Marius Fieschi
Affiliations

Design and validation of an automated method to detect known adverse drug reactions in MEDLINE: a contribution from the EU-ADR project

Paul Avillach et al. J Am Med Inform Assoc. .

Abstract

Objectives: The aim of this research was to automate the search of publications concerning adverse drug reactions (ADR) by defining the queries used to search MEDLINE and by determining the required threshold for the number of extracted publications to confirm the drug/event association in the literature.

Methods: We defined an approach based on the medical subject headings (MeSH) 'descriptor records' and 'supplementary concept records' thesaurus, using the subheadings 'chemically induced' and 'adverse effects' with the 'pharmacological action' knowledge. An expert-built validation set of true positive and true negative drug/adverse event associations (n=61) was used to validate our method.

Results: Using a threshold of three of more extracted publications, the automated search method presented a sensitivity of 90% and a specificity of 100%. For nine different drug/event pairs selected, the recall of the automated search ranged from 24% to 64% and the precision from 93% to 48%.

Conclusions: This work presents a method to find previously established relationships between drugs and adverse events in the literature. Using MEDLINE, following a MeSH approach to filter the signals, is a valid option. Our contribution is available as a web service that will be integrated in the final European EU-ADR project (Exploring and Understanding Adverse Drug Reactions by integrative mining of clinical records and biomedical knowledge) automated system.

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Figures

Figure 1
Figure 1
Receiver operating characteristic curve of the model.
Figure 2
Figure 2
Number of times upper gastrointestinal bleeding (UGIB) and piroxicam are seen together in MEDLINE in the context of adverse drug reactions by year. This figure is only reproduced in colour in the online version.
Figure 3
Figure 3
Number of times upper gastrointestinal bleeding (UGIB) and celecoxib are seen together in MEDLINE in the context of adverse drug reactions  by year. This figure is only reproduced in colour in the online version.
Figure 4
Figure 4
Web service workflow in Taverna. This figure is only reproduced in colour in the online version.
See this image and copyright information in PMC

References

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