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Extracting drug-drug interactions from biomedical texts
© Segura-Bedmar et al; licensee BioMed Central Ltd. 2010
Published: 06 October 2010
A drug-drug interaction (DDI) occurs when one drug influences the level or activity of another drug. The detection of drug interactions is an important research area in patient safety since these interactions can become very dangerous and increase health care costs. Although there are different databases supporting health care professionals in the detection of drug interactions, this kind of resources is rarely complete. Drug interactions are frequently reported in journals of clinical pharmacology, making medical literature the most effective source for the detection of drug interactions. However, the increasing volume of the literature overwhelms health care professionals trying to keep an up-to-date collection of all reported DDIs. The development of automatic methods for collecting, maintaining and interpreting this information is crucial for achieving a real improvement in their early detection. 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 drug-drug interactions from biomedical texts.
Materials and methods
While the first approximation based on pattern matching achieves low performance (Precision=48.7%, Recall=25.7%, F-measure=33.6%), the approach based on kernel-methods achieves better performance, especially better recall (Precision=55.1%, Recall=82.3%, F-measure=66.0%). The variability of natural language makes it difficult for our first approach to accurately detect all drug-drug interactions occurring in texts since sentences conveying the same relation may be lexically and syntactically composed in different ways. Inversely, sentences that are lexically common may not necessarily convey the same relation. Therefore, the set of lexical patterns proposed by our pharmacist is not enough to identify many of the interactions. Performance achieved by the kernel-based approach is comparable to studies which have carried out a similar task such as the extraction of protein-protein interactions.
To the best of our knowledge, this work has proposed the first integral solution for the automatic extraction of DDI from biomedical texts. We hope that our proposal and the DrugDDI corpus contribute to the development of useful tools to assist healthcare professionals in the early detection of DDIs.
This work has been partially supported by the Spanish research projects: MAVIR consortium (MA2VICMR S2009/TIC-1542, http://www.mavir.net), a network of excellence funded by the Madrid Regional Government and TIN2007-67407-C03-01 (BRAVO: Advanced Multimodal and Multilingual Question Answering). The authors are grateful to María Segura Bedmar, manager of the Drug Information Center of the Mostoles University Hospital, Spain, for her valuable assistance in the annotation of the corpus and evaluation of the system.
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