Using a shallow linguistic kernel for drug–drug interaction extraction

Abstract

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.

Proceedings of the 4th BioNLP Shared Task Workshop

“…Each pair of entities that satisfied the argument type restrictions was considered a candidate pair. This kernel has been applied to biomedical text, for the extraction of relations between proteins (Tikk et al, 2010) and chemical compounds (Segura-Bedmar et al, 2011), obtaining positive results. The shallow linguistic kernel is a composite sequence kernel which uses both a local and global context window, which we set at 3 and 4, respectively.…”

Automatic extraction of Drug-Drug interaction from literature through detecting clause dependency and linguistic-based negation

“…However, most of the previous literatures [28] suggest that, rather than simple sentences of single clause, more errors are to be produced by complex compound sentences which are, by the way, very common in the biomedical literature. Describing global context kernel, local context kernel, and subtree kernel, respectively, the three tables represent complex and compound sentences which are very commonly used in biomedical literature to produce higher error rates than those of simple sentences with just one clause.…”

Harnessing Social Media for Drug-Drug Interactions Detection

“…The underlying assumption is that if drug A and drug B interact to produce a specific biological effect, then drugs similar to drug A (or drug B) are likely to interact with drug B (or drug A) to produce the same effect. Segura-Bedmar et al [25][26][27] proposed two different methods -pattern matching and supervised machine learning (shallow linguistic kernel) -to automatically extract DDIs from biomedical texts retrieved from DrugBank. In order to evaluate their methods, they created the first annotated corpus, the DrugDDI corpus using documents in DrugBank, to study the phenomenon of interactions among drugs.…”