{"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-assistant-diagnosis-for-electronic","title":"Clinical Assistant Diagnosis for Electronic Medical Record Based on Convolutional Neural Network","arxiv_id":"1804.08261","date":"2018-04-23","proceeding":null,"authors":["Zhongliang Yang","Yongfeng Huang","Yiran Jiang","Yuxi Sun","Yu-Jin Zhan","Pengcheng Luo"],"abstract":"Automatically extracting useful information from electronic medical records\nalong with conducting disease diagnoses is a promising task for both clinical\ndecision support(CDS) and neural language processing(NLP). Most of the existing\nsystems are based on artificially constructed knowledge bases, and then\nauxiliary diagnosis is done by rule matching. In this study, we present a\nclinical intelligent decision approach based on Convolutional Neural\nNetworks(CNN), which can automatically extract high-level semantic information\nof electronic medical records and then perform automatic diagnosis without\nartificial construction of rules or knowledge bases. We use collected 18,590\ncopies of the real-world clinical electronic medical records to train and test\nthe proposed model. Experimental results show that the proposed model can\nachieve 98.67\\% accuracy and 96.02\\% recall, which strongly supports that using\nconvolutional neural network to automatically learn high-level semantic\nfeatures of electronic medical records and then conduct assist diagnosis is\nfeasible and effective.","url_abs":"http://arxiv.org/abs/1804.08261v1","url_pdf":"http://arxiv.org/pdf/1804.08261v1.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-assistant-diagnosis-for-electronic","repo_url":"https://github.com/YangzlTHU/C-EMRs","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"none","reach":null}],"tasks":[],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}