2012
DOI: 10.1136/amiajnl-2012-001083
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Design and validation of an automated method to detect known adverse drug reactions in MEDLINE: a contribution from the EU–ADR project

Abstract: 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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Cited by 54 publications
(50 citation statements)
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“…The classifiers in those two studies were constructed based on a set of manually selected ontological and textual features and MeSH terms. Avillach et al 16 used MeSH terms (ie, 'chemically-induced,' 'adverse events') to automate the MEDLINE search to determine if a give drug-SE association was already reported in the literature. There are two perceived limitations in using MeSH terms alone in cancer drug SE extraction: first, cancer drug SE information often appears together with drug-disease treatment information in the same articles.…”
Section: Introductionmentioning
confidence: 99%
“…The classifiers in those two studies were constructed based on a set of manually selected ontological and textual features and MeSH terms. Avillach et al 16 used MeSH terms (ie, 'chemically-induced,' 'adverse events') to automate the MEDLINE search to determine if a give drug-SE association was already reported in the literature. There are two perceived limitations in using MeSH terms alone in cancer drug SE extraction: first, cancer drug SE information often appears together with drug-disease treatment information in the same articles.…”
Section: Introductionmentioning
confidence: 99%
“…[10] Methods and tools, known as data mining, have been developed over the years to analyse large sets of data. Data mining methods in pharmacovigilance have been used with several goals, [8,9] (i) in order to automate the search of publications concerning ADR in Medline, [11] (ii) to correlate and predict post-marketing adverse drug effects based on screening data from public databases of chemical structures like Pubchem, [12] (iii) to develop new algorithms to detect new or latent multi-drug adverse events in Adverse Event Reporting System database [5] and (iv) to find out new pharmacovigilance signal by mining EHR data. [13,14] For example, in a recently published work, [13] Tatonetti NP et al…”
Section: Introductionmentioning
confidence: 99%
“…Recently, Avillach et al [13] designed a method to automatically detect known adverse drug reactions in MEDLINE, based on Medical Subject Headings (MeSH) including "descriptors", "supplementary concepts" with subheadings such as "chemically induced", "adverse effects" and "pharmacological action". based discovery for novel therapeutic approaches can be found in [59].…”
Section: Text Mining On Medical Literaturementioning
confidence: 99%
“…To cope with this challenge, on the one hand, methods have been developed to improve document retrieval from biomedical literature databases [78,136,54,13] based discovery for novel therapeutic approaches can be found in [59].…”
Section: Problems and Challengesmentioning
confidence: 99%
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