J Biomed Inform - Using a shallow linguistic kernel for drug-drug interaction extraction.

Tópicos

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Resumo

A drug-drug interaction (DDI) occurs when one drug influences the level or activity of another drug. Information Extraction (IE) techniques can provide health care professionals with an interesting way to reduce time spent reviewing the literature for potential drug-drug interactions. Nevertheless, no approach has been proposed to the problem of extracting DDIs in biomedical texts. In this article, we study whether a machine learning-based method is appropriate for DDI extraction in biomedical texts and whether the results provided are superior to those obtained from our previously proposed pattern-based approach. The method proposed here for DDI extraction is based on a supervised machine learning technique, more specifically, the shallow linguistic kernel proposed in Giuliano et al. (2006). Since no benchmark corpus was available to evaluate our approach to DDI extraction, we created the first such corpus, DrugDDI, annotated with 3169 DDIs. We performed several experiments varying the configuration parameters of the shallow linguistic kernel. The model that maximizes the F-measure was evaluated on the test data of the DrugDDI corpus, achieving a precision of 51.03%, a recall of 72.82% and an F-measure of 60.01%. To the best of our knowledge, this work has proposed the first full solution for the automatic extraction of DDIs from biomedical texts. Our study confirms that the shallow linguistic kernel outperforms our previous pattern-based approach. Additionally, it is our hope that the DrugDDI corpus will allow researchers to explore new solutions to the DDI extraction problem.

Resumo Limpo

drugdrug interact ddi occur one drug influenc level activ anoth drug inform extract ie techniqu can provid health care profession interest way reduc time spent review literatur potenti drugdrug interact nevertheless approach propos problem extract ddis biomed text articl studi whether machin learningbas method appropri ddi extract biomed text whether result provid superior obtain previous propos patternbas approach method propos ddi extract base supervis machin learn techniqu specif shallow linguist kernel propos giuliano et al sinc benchmark corpus avail evalu approach ddi extract creat first corpus drugddi annot ddis perform sever experi vari configur paramet shallow linguist kernel model maxim fmeasur evalu test data drugddi corpus achiev precis recal fmeasur best knowledg work propos first full solut automat extract ddis biomed text studi confirm shallow linguist kernel outperform previous patternbas approach addit hope drugddi corpus will allow research explor new solut ddi extract problem

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