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This article is part of the supplement: Fourth International Workshop on Data and Text Mining in Biomedical Informatics (DTMBio) 2010

Open Access Proceedings

A linguistic rule-based approach to extract drug-drug interactions from pharmacological documents

Isabel Segura-Bedmar*, Paloma Martínez and César de Pablo-Sánchez

Author Affiliations

Computer Science Department, University Carlos III of Madrid, Leganés, 28911, Spain

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BMC Bioinformatics 2011, 12(Suppl 2):S1  doi:10.1186/1471-2105-12-S2-S1

Published: 29 March 2011

Abstract

Background

A drug-drug interaction (DDI) occurs when one drug influences the level or activity of another drug. The increasing volume of the scientific literature overwhelms health care professionals trying to be kept up-to-date with all published studies on DDI.

Methods

This paper describes a hybrid linguistic approach to DDI extraction that combines shallow parsing and syntactic simplification with pattern matching. Appositions and coordinate structures are interpreted based on shallow syntactic parsing provided by the UMLS MetaMap tool (MMTx). Subsequently, complex and compound sentences are broken down into clauses from which simple sentences are generated by a set of simplification rules. A pharmacist defined a set of domain-specific lexical patterns to capture the most common expressions of DDI in texts. These lexical patterns are matched with the generated sentences in order to extract DDIs.

Results

We have performed different experiments to analyze the performance of the different processes. The lexical patterns achieve a reasonable precision (67.30%), but very low recall (14.07%). The inclusion of appositions and coordinate structures helps to improve the recall (25.70%), however, precision is lower (48.69%). The detection of clauses does not improve the performance.

Conclusions

Information Extraction (IE) techniques can provide an interesting way of reducing the time spent by health care professionals on reviewing the literature. Nevertheless, no approach has been carried out to extract DDI from texts. To the best of our knowledge, this work proposes the first integral solution for the automatic extraction of DDI from biomedical texts.