{"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/clinical-concept-extraction-with-contextual","title":"Clinical Concept Extraction with Contextual Word Embedding","arxiv_id":"1810.10566","date":"2018-10-24","proceeding":null,"authors":["Henghui Zhu","Ioannis Ch. Paschalidis","Amir Tahmasebi"],"abstract":"Automatic extraction of clinical concepts is an essential step for turning\nthe unstructured data within a clinical note into structured and actionable\ninformation. In this work, we propose a clinical concept extraction model for\nautomatic annotation of clinical problems, treatments, and tests in clinical\nnotes utilizing domain-specific contextual word embedding. A contextual word\nembedding model is first trained on a corpus with a mixture of clinical reports\nand relevant Wikipedia pages in the clinical domain. Next, a bidirectional\nLSTM-CRF model is trained for clinical concept extraction using the contextual\nword embedding model. We tested our proposed model on the I2B2 2010 challenge\ndataset. Our proposed model achieved the best performance among reported\nbaseline models and outperformed the state-of-the-art models by 3.4% in terms\nof F1-score.","url_abs":"http://arxiv.org/abs/1810.10566v2","url_pdf":"http://arxiv.org/pdf/1810.10566v2.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":"clinical-concept-extraction-with-contextual","repo_url":"https://github.com/noc-lab/clinical_concept_extraction","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"tf","reach":null}],"tasks":[{"task_slug":"clinical-concept-extraction","task_name":"Clinical Concept Extraction"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":"https://syntology.ai/paper/1810.10566","atlas_url":"https://app.syntology.ai/?focus=1810.10566","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1810.10566"}},"developers":"https://syntology.ai/developers","read_at":"2026-09-25T09:33:49+00:00","read_at_is":"when the build read Syntology's graph, not when any sample ran","claim":"Per-sample execution status on synthesized fixtures; not a correctness claim about the paper. Samples come from repositories linked to the paper, official or community; repo_kind says which.","repos":[{"provenance":"external:paperswithcode_snapshot_2025-07-28","url":"https://github.com/noc-lab/clinical_concept_extraction","reach":null}],"summary":{"ran_draft_wrong":1},"by_repo_kind":{"official":{"samples":1,"ran":1,"repositories":1}},"repo_kind_vocabulary":{"official":"The archive marks this repository official for the paper","named_in_paper":"The archive records that the paper mentions this repository; it is not marked official","listed":"In the archive's code links for this paper, not marked official and not recorded as mentioned in the paper","found_in_text":"Syntology found this repository in the paper's own text; whether it is the authors' implementation is not asserted","community":"Not in the archive's code links for this paper; a community repository Syntology harvested"},"n_pointer_only_for_licence":0,"samples":[{"code_sha256_prefix":"373da13ca09433a3","entry":"find_marco_f1","repo":"noc-lab/clinical_concept_extraction","repo_kind":"official","path":"training_scripts/training.py","file_url":"https://github.com/noc-lab/clinical_concept_extraction/blob/HEAD/training_scripts/training.py","link_basis":"first_harvest_node","language":"python","status":"ran_draft_wrong","verification_level":1,"contract_check":"MISDECLARED","metamorphic_tier":"well_formed","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"mcp_get_code":{"code_sha256":"373da13ca09433a3"}}]},"arxiv_metadata":null,"syntology_extracted_results":null}