{"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-1","title":"Bidirectional LSTM-CRF for Clinical Concept Extraction","arxiv_id":"1610.05858","date":"2016-10-19","proceeding":"WS 2016 12","authors":["Raghavendra Chalapathy","Ehsan Zare Borzeshi","Massimo Piccardi"],"abstract":"Extraction of concepts present in patient clinical records is an essential\nstep in clinical research. The 2010 i2b2/VA Workshop on Natural Language\nProcessing Challenges for clinical records presented concept extraction (CE)\ntask, with aim to identify concepts (such as treatments, tests, problems) and\nclassify them into predefined categories. State-of-the-art CE approaches\nheavily rely on hand crafted features and domain specific resources which are\nhard to collect and tune. For this reason, this paper employs bidirectional\nLSTM with CRF decoding initialized with general purpose off-the-shelf word\nembeddings for CE. The experimental results achieved on 2010 i2b2/VA reference\nstandard corpora using bidirectional LSTM CRF ranks closely with top ranked\nsystems.","url_abs":"http://arxiv.org/abs/1610.05858v1","url_pdf":"http://arxiv.org/pdf/1610.05858v1.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-1","repo_url":"https://github.com/raghavchalapathy/Bidirectional-LSTM-CRF-for-Clinical-Concept-Extraction","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"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":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}