{"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/tcmpr-tcm-prescription-recommendation-based","title":"TCMPR: TCM Prescription recommendation based on subnetwork term mapping and deep learning","arxiv_id":null,"date":"2021-12-09","proceeding":"2021 IEEE International Conference on Bioinformatics and Biomedicine (BIBM) 2021 12","authors":["Xin Dong","Yi Zheng","Zixin Shu","Kai Chang","Dengying Yan","Jianan Xia","Qiang Zhu","Kunyu Zhong","Xinyan Wang","Kuo Yang","Xuezhong Zhou"],"abstract":"Traditional Chinese medicine (TCM) has played an indispensable role in clinical diagnose and treatment. Based on patient’s symptom phenotypes, computation-based prescription recommendation methods can recommend personalized TCM prescription using machine learning and artificial intelligence technologies. However, owing to the complexity and individuation of patient’s clinical phenotypes, current prescription recommendation methods cannot obtain good performance. Meanwhile, it’s very difficult to conduct effective representation for unrecorded symptom terms in existing knowledge base. In this study, we proposed a subnetwork-based symptom term mapping method (SSTM), and constructed a SSTM-based TCM prescription recommendation method (termed TCMPR). Our SSTM can extract the subnetwork structure between symptoms from knowledge network to effectively represent the embedding features of clinical symptom terms (especially, the unrecorded terms). The experimental results showed that our method performs better than state-of-the-art methods. In addition, the comprehensive experiments of TCMPR with different hyper parameters (i.e., feature embedding, feature dimension and feature fusion) that demonstrates that our method has high performance on TCM prescription recommendation and potentially promote clinical diagnosis and treatment of TCM precision medicine.","url_abs":"https://ieeexplore.ieee.org/document/9669588/","url_pdf":"https://ieeexplore.ieee.org/document/9669588/","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":"tcmpr-tcm-prescription-recommendation-based","repo_url":"https://github.com/dxbjtu1228/TCMPR","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"tf","reach":null}],"tasks":[],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":null,"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}