{"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/code-synonyms-do-matter-multiple-synonyms-1","title":"Code Synonyms Do Matter: Multiple Synonyms Matching Network for Automatic ICD Coding","arxiv_id":"2203.01515","date":"2022-03-03","proceeding":"ACL 2022 5","authors":["Zheng Yuan","Chuanqi Tan","Songfang Huang"],"abstract":"Automatic ICD coding is defined as assigning disease codes to electronic medical records (EMRs). Existing methods usually apply label attention with code representations to match related text snippets. Unlike these works that model the label with the code hierarchy or description, we argue that the code synonyms can provide more comprehensive knowledge based on the observation that the code expressions in EMRs vary from their descriptions in ICD. By aligning codes to concepts in UMLS, we collect synonyms of every code. Then, we propose a multiple synonyms matching network to leverage synonyms for better code representation learning, and finally help the code classification. Experiments on the MIMIC-III dataset show that our proposed method outperforms previous state-of-the-art methods.","url_abs":"https://arxiv.org/abs/2203.01515v2","url_pdf":"https://arxiv.org/pdf/2203.01515v2.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":"code-synonyms-do-matter-multiple-synonyms-1","repo_url":"https://github.com/ganjinzero/icd-msmn","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"pytorch","reach":{"status":"ok"}}],"tasks":[{"task_slug":"medical-code-prediction","task_name":"Medical Code Prediction"},{"task_slug":"representation-learning","task_name":"Representation Learning"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/medical-code-prediction-on-mimic-iii","task":"Medical Code Prediction","dataset":"MIMIC-III","model":"MSMN","rank_in_archive_order":7,"of":18,"metrics":{"Macro-AUC":"95.0","Macro-F1":"10.3","Micro-AUC":"99.2","Micro-F1":"58.4","Precision@15":"59.9","Precision@8":"75.2"},"uses_additional_data":false}],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=2203.01515","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}