The DDIExtraction Shared Task 2013 is the second edition of the DDIExtraction Shared Task series, a community-wide effort to promote the implementation and comparative assessment of natural language processing (NLP) techniques in the field of the pharmacovigilance domain, in particular, to address the extraction of drug-drug interactions (DDI) from biomedical texts. This edition has been the first attempt to compare the performance of Information Extraction (IE) techniques specific for each of the basic steps of the DDI extraction pipeline. To attain this aim, two main tasks were proposed: the recognition and classification of pharmacological substances and the detection and classification of drug-drug interactions. DDIExtraction 2013 was held from January to June 2013 and attracted wide attention with a total of 14 teams (6 of the teams participated in the drug name recognition task, while 8 participated in the DDI extraction task) from 7 different countries. For the task of the recognition and classification of pharmacological names, the best system achieved an F1 of 71.5%, while, for the detection and classification of DDIs, the best result was an F1 of 65.1%. The results show advances in the state of the art and demonstrate that significant challenges remain to be resolved. This paper focuses on the second task (extraction of DDIs) and examines its main challenges, which have yet to be resolved.
drug interaction; information extraction; relation extraction; information retrieval; relation extraction; drug interactions; acetylsalicylic acid; aldesulfone; astemizole; barbituric acid derivative; cisapride; clarithromycin; doxorubicin; erythromycin; ferrous gluconate; ferrous sulfate; furosemide; guanethidine; itraconazole; macrolide; meloxicam; mercaptopurine; methyldopa; metolazone; miconazole; mirtazapine; nonsteroid antiinflammatory agent; oxybutynin; penicillin derivative; pimozide; selegiline; streptokinase; tetracycline; tolbutamide; unindexed drug; uricosuric agent; article; data extraction; drug information; drug interaction; drug surveillance program; knowledge base; learning algorithm; machine learning; medline; natural language processing; support vector machine