{"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/multi-label-classification-of-patient-notes-a","title":"Multi-Label Classification of Patient Notes a Case Study on ICD Code Assignment","arxiv_id":"1709.09587","date":"2017-09-27","proceeding":null,"authors":["Tal Baumel","Jumana Nassour-Kassis","Raphael Cohen","Michael Elhadad","No`emie Elhadad"],"abstract":"In the context of the Electronic Health Record, automated diagnosis coding of\npatient notes is a useful task, but a challenging one due to the large number\nof codes and the length of patient notes. We investigate four models for\nassigning multiple ICD codes to discharge summaries taken from both MIMIC II\nand III. We present Hierarchical Attention-GRU (HA-GRU), a hierarchical\napproach to tag a document by identifying the sentences relevant for each\nlabel. HA-GRU achieves state-of-the art results. Furthermore, the learned\nsentence-level attention layer highlights the model decision process, allows\neasier error analysis, and suggests future directions for improvement.","url_abs":"http://arxiv.org/abs/1709.09587v3","url_pdf":"http://arxiv.org/pdf/1709.09587v3.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":"multi-label-classification-of-patient-notes-a","repo_url":"https://github.com/talbaumel/MIMIC","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"none","reach":{"status":"ok"}}],"tasks":[{"task_slug":"classification","task_name":"General Classification"},{"task_slug":"multi-label-classification-2","task_name":"MUlTI-LABEL-ClASSIFICATION"},{"task_slug":"multi-label-classification","task_name":"Multi-Label Classification"},{"task_slug":"sentence","task_name":"Sentence"},{"task_slug":"tag","task_name":"TAG"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1709.09587","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}