A novel feature-based approach to extract drug-drug interactions from biomedical text

MOTIVATION Knowledge of drug-drug interactions (DDIs) is crucial for health-care professionals to avoid adverse effects when co-administering drugs to patients. As most newly discovered DDIs are made available through scientific publications, automatic DDI extraction is highly relevant. RESULTS We propose a novel feature-based approach to extract DDIs from text. Our approach consists of three steps. First, we apply text preprocessing to convert input sentences from a given dataset into structured representations. Second, we map each candidate DDI pair from that dataset into a suitable syntactic structure. Based on that, a novel set of features is used to generate feature vectors for these candidate DDI pairs. Third, the obtained feature vectors are used to train a support vector machine (SVM) classifier. When evaluated on two DDI extraction challenge test datasets from 2011 and 2013, our system achieves F-scores of 71.1% and 83.5%, respectively, outperforming any state-of-the-art DDI extraction system. AVAILABILITY AND IMPLEMENTATION The source code is available for academic use at http://www.biosemantics.org/uploads/DDI.zip.

[1]  K. Bretonnel Cohen,et al.  The structural and content aspects of abstracts versus bodies of full text journal articles are different , 2010, BMC Bioinformatics.

[2]  Peter M. A. Sloot,et al.  A hybrid approach to extract protein-protein interactions , 2011, Bioinform..

[3]  César de Pablo-Sánchez,et al.  Using a shallow linguistic kernel for drug-drug interaction extraction , 2011, J. Biomed. Informatics.

[4]  Peter M. A. Sloot,et al.  A robust approach to extract biomedical events from literature , 2012, Bioinform..

[5]  Taghi M. Khoshgoftaar,et al.  Experimental perspectives on learning from imbalanced data , 2007, ICML '07.

[6]  Jun'ichi Tsujii,et al.  A Rich Feature Vector for Protein-Protein Interaction Extraction from Multiple Corpora , 2009, EMNLP.

[7]  Hongfei Lin,et al.  Extracting Drug-Drug Interaction from the Biomedical Literature Using a Stacked Generalization-Based Approach , 2013, PloS one.

[8]  Alberto Lavelli,et al.  FBK-irst : A Multi-Phase Kernel Based Approach for Drug-Drug Interaction Detection and Classification that Exploits Linguistic Information , 2013, *SEMEVAL.

[9]  Ulf Leser,et al.  A detailed error analysis of 13 kernel methods for protein–protein interaction extraction , 2013, BMC Bioinformatics.

[10]  Claudio Giuliano,et al.  Exploiting Shallow Linguistic Information for Relation Extraction from Biomedical Literature , 2006, EACL.

[11]  Supinya Dechanont,et al.  Hospital admissions/visits associated with drug–drug interactions: a systematic review and meta‐analysis , 2014, Pharmacoepidemiology and drug safety.

[12]  Isabel Segura-Bedmar,et al.  The 1st DDIExtraction-2011 challenge task: Extraction of Drug-Drug Interactions from biomedical texts , 2011 .

[13]  K. Bretonnel Cohen,et al.  Mining the pharmacogenomics literature - a survey of the state of the art , 2012, Briefings Bioinform..

[14]  Alberto Lavelli,et al.  Exploiting the Scope of Negations and Heterogeneous Features for Relation Extraction: A Case Study for Drug-Drug Interaction Extraction , 2013, HLT-NAACL.

[15]  Chitta Baral,et al.  Discovering drug–drug interactions: a text-mining and reasoning approach based on properties of drug metabolism , 2010, Bioinform..

[16]  Mariana L. Neves,et al.  WBI-DDI: Drug-Drug Interaction Extraction using Majority Voting , 2013, *SEMEVAL.

[17]  M. A. van de Laar,et al.  An evidence-based assessment of the clinical significance of drug-drug interactions between disease-modifying antirheumatic drugs and non-antirheumatic drugs according to rheumatologists and pharmacists. , 2009, Clinical therapeutics.

[18]  Juliane Fluck,et al.  SCAI: Extracting drug-drug interactions using a rich feature vector , 2013, SemEval@NAACL-HLT.

[19]  Paloma Martínez,et al.  The DDI corpus: An annotated corpus with pharmacological substances and drug-drug interactions , 2013, J. Biomed. Informatics.

[20]  Ulf Leser,et al.  Relation Extraction for Drug-Drug Interactions using Ensemble Learning , 2011 .

[21]  Alessandro Moschitti,et al.  A Study on Convolution Kernels for Shallow Statistic Parsing , 2004, ACL.

[22]  Paloma Martínez,et al.  SemEval-2013 Task 9 : Extraction of Drug-Drug Interactions from Biomedical Texts (DDIExtraction 2013) , 2013, *SEMEVAL.

[23]  Yu Ko,et al.  Clinically Significant Drug–Drug Interactions Between Oral Anticancer Agents and Nonanticancer Agents: Profiling and Comparison of Two Drug Compendia , 2008, The Annals of pharmacotherapy.

[24]  Sampo Pyysalo,et al.  Overview of BioNLP Shared Task 2013 , 2013, BioNLP@ACL.

[25]  D. Rebholz-Schuhmann,et al.  Text-mining solutions for biomedical research: enabling integrative biology , 2012, Nature Reviews Genetics.