{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/bidirectional-lstm-crf-for-clinical-concept","title":"Bidirectional LSTM-CRF for Clinical Concept Extraction","arxiv_id":"1611.08373","date":"2016-11-25","proceeding":null,"authors":["Raghavendra Chalapathy","Ehsan Zare Borzeshi","Massimo Piccardi"],"abstract":"Automated extraction of concepts from patient clinical records is an\nessential facilitator of clinical research. For this reason, the 2010 i2b2/VA\nNatural Language Processing Challenges for Clinical Records introduced a\nconcept extraction task aimed at identifying and classifying concepts into\npredefined categories (i.e., treatments, tests and problems). State-of-the-art\nconcept extraction approaches heavily rely on handcrafted features and\ndomain-specific resources which are hard to collect and define. For this\nreason, this paper proposes an alternative, streamlined approach: a recurrent\nneural network (the bidirectional LSTM with CRF decoding) initialized with\ngeneral-purpose, off-the-shelf word embeddings. The experimental results\nachieved on the 2010 i2b2/VA reference corpora using the proposed framework\noutperform all recent methods and ranks closely to the best submission from the\noriginal 2010 i2b2/VA challenge.","url_abs":"http://arxiv.org/abs/1611.08373v1","url_pdf":"http://arxiv.org/pdf/1611.08373v1.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"bidirectional-lstm-crf-for-clinical-concept","repo_url":"https://github.com/raghavchalapathy/Bidirectional-LSTM-CRF-for-Clinical-Concept-Extraction","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"none","reach":null}],"tasks":[{"task_slug":"clinical-concept-extraction","task_name":"Clinical Concept Extraction"},{"task_slug":"word-embeddings","task_name":"Word Embeddings"}],"methods":[{"method_slug":"crf","method_name":"CRF"},{"method_slug":"lstm","method_name":"LSTM"},{"method_slug":"sigmoid-activation","method_name":"Sigmoid Activation"},{"method_slug":"tanh-activation","method_name":"Tanh Activation"}],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":"https://syntology.ai/paper/1611.08373","atlas_url":"https://app.syntology.ai/?focus=1611.08373","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}