Application of information extraction techniques to pharmalogical domain: extracting drug-drug interaction

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dc.contributor.author Segura Bedmar, Isabel
dc.date.accessioned 2015-05-14T10:32:28Z
dc.date.available 2015-05-14T10:32:28Z
dc.date.issued 2011-01
dc.identifier.bibliographicCitation Sociedad Española para el Procesamiento del Lenguaje Natural (SEPLN). Monografías 10 (2011) September
dc.identifier.isbn 978-84-694-53
dc.identifier.uri http://hdl.handle.net/10016/20711
dc.description.abstract A drug-drug interaction 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 resource 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 drugdrug interactions. 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. We have conducted a detailed study on various IE techniques applied to biomedical domain. Based on this study, we have proposed two different approximations for the extraction of drug-drug interactions from texts. The first approximation proposes a hybrid approach, which combines shallow parsing and pattern matching to extract relations between drugs from biomedical texts. The second approximation is based on a supervised machine learning approach, in particular, kernel methods. In addition, we have created and annotated the first corpus, DrugDDI, annotated with drug-drug interactions, which allow us to evaluate and compare both approximations. To the best of our knowledge, the DrugDDI corpus is the only available corpus annotated for drug-drug interactions and this research is the first work addressing the problem of extracting drug-drug interactions from biomedical texts. We believe the DrugDDI corpus is an important contribution because it could encourage other research groups to research into this problem. We have also defined three auxiliary processes to provide crucial information, which will be used by the aforementioned approximations. These auxiliary tasks are as follows: (1) a process for text analysis based on the UMLS MetaMap Transfer tool (MMTx) to provide shallow syntactic and semantic information from texts, (2) a process for drug name recognition and classification, and (3) a process for drug anaphora resolution. Finally, we have developed a pipeline prototype which integrates the different auxiliary processes. The pipeline architecture allows us to easily integrate these modules with each of the approaches proposed in this work: pattern-matching or kernels. Several experiments were performed on the DrugDDI corpus. They show that while the first approximation based on pattern matching achieves low performance, the approach based on kernel-methods achieves a performance comparable to those obtained by approaches which carry out a similar task such as the extraction of protein-protein interactions.
dc.description.sponsorship This work has been partially supported by the Spanish research projects: MAVIR consortium (S- 0505/TIC-0267, 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).
dc.format.extent 153
dc.format.mimetype application/pdf
dc.language.iso eng
dc.publisher Sociedad Española para el Procesamiento del Lenguaje Natural
dc.relation.ispartofseries Monografías SEPLN
dc.rights © 2011 Isabel Segura-Bedmar
dc.title Application of information extraction techniques to pharmalogical domain: extracting drug-drug interaction
dc.type book
dc.description.status Publicado
dc.relation.publisherversion http://www.sepln.org/wp-content/uploads/2011/09/monografia-sepln10.pdf
dc.subject.eciencia Informática
dc.rights.accessRights openAccess
dc.type.version publishedVersion
dc.relation.eventtype proceeding
dc.identifier.publicationfirstpage 1
dc.identifier.publicationissue 10, September
dc.identifier.publicationlastpage 152
dc.identifier.publicationtitle Sociedad Española para el Procesamiento del Lenguaje Natural (SEPLN). Monografías
dc.identifier.uxxi LM/0000002002
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