Journal of Biomedical Informatics

Text mining for pharmacovigilance: Using machine learning for drug name recognition and drug-drug interaction extraction and classification

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Pharmacovigilance (PV) is defined by the World Health Organization as the science and activities related to the detection, assessment, understanding and prevention of adverse effects or any other drug-related problem. An essential aspect in PV is to acquire knowledge about Drug-Drug Interactions (DDIs). The shared tasks on DDI-Extraction organized in 2011 and 2013 have pointed out the importance of this issue and provided benchmarks for: Drug Name Recognition, DDI extraction and DDI classification. In this paper, we present our text mining systems for these tasks and evaluate their results on the DDI-Extraction benchmarks. Our systems rely on machine learning techniques using both feature-based and kernel-based methods. The obtained results for drug name recognition are encouraging. For DDI-Extraction, our hybrid system combining a feature-based method and a kernel-based method was ranked second in the DDI-Extraction-2011 challenge, and our two-step system for DDI detection and classification was ranked first in the DDI-Extraction-2013 task at SemEval. We discuss our methods and results and give pointers to future work.