TAKELAB: Medical Information Extraction and Linking with MINERAL

Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pages 389–393, Denver, Colorado, June 4-5, 2015. c⃝2015 Association for Computational Linguistics TAKELAB: Medical Information Extraction and Linking with MINERAL Goran Glavaˇs University of Zagreb Faculty of Electrical Engineering and Computing Text Analysis and Knowledge Engineering Lab Unska 3, 10000 Zagreb, Croatia goran.glavas@fer.hr Abstract Medical texts are filled with mentions of dis- eases, disorders, and other clinical conditions, with many different surface forms relating to the same condition. We describe MINERAL, a system for extraction and normalization of dis- ease mentions in clinical text, with which we participated in the Task 14 of SemEval 2015 evaluation campaign. MINERAL relies on a conditional random fields-based model with a rich set of features for mention detection, and a semantic textual similarity measure for entity linking. MINERAL reaches joint extraction and linking performance of 75.9% relaxed F1- score (strict score of 72.7%) and ranks fourth among 16 participating teams. 1 Introduction Clinical narratives contain numerous mentions of diseases and disorders. Recognizing these mentions in text and normalizing the different superficial forms of a disorder to the same canonical form could enable new types of analyses that would be beneficial for both medical professionals and patients. Detection and normalization of various concepts such as named entities (McCallum and Li, 2003; Kr- ishnan and Manning, 2006) or events (Bethard, 2013; Glavaˇs and ˇSnajder, 2014) has long been in the focus of the NLP community. Disorder mentions in clini- cal text, however, have some peculiarities not typical for traditional information extraction tasks such as discontinuity or distributivity of a single token to multiple disorder mentions. For example, the snippet “Patient’s extremities were turned in and clinched together as a consequence of...” contains two mentions of medical conditions, “ex- tremities turned in” and “extremities clinched to- gether”, which share the token “extremities”, with the latter mention being discontinuous. In this paper we present the MINERAL (Medi- cal INformation ExtRAction and Linking) system for recognizing and normalizing mentions of clinical conditions, with which we participated in Task 14 of SemEval 2015 evaluation campaign. The system recognizes disorder mentions via the supervised con- ditional random fields (CRF) model with a rich set of lexical, gazetteer-based, and informativeness-based features. We apply a set of post-processing rules to construct disorder mentions from token-level anno- tations which follow the BEGIN-INSIDE-OUTSIDE scheme. We utilize a measure of semantic textual similarity to link recognized disorder mentions to entries in the SNOMED-CT medical database. Our approach is resource light in the sense that, except for SNOMED-CT which is necessary for normalization, it does not rely on medical NLP resources. We ranked fourth (relaxed evaluation setting) among 16 teams in the official evaluation, with 3% lower performance than the best-performing system. Such a result suggests that coupling sequence la- belling for mention recognition with an STS measure for concept normalization poses a viable solution for entity recognition in the clinical domain. We make the MINERAL system freely available.1 1http://takelab.fer.hr/mineral 389 2 Clinical Information Extraction Clinical …

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