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However, almost all previous HPR methods have not adhered to the clinical principles of syndrome differentiation and treatment planning of TCM, which has resulted in suboptimal performance and difficulties in application to real-world clinical scenarios.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Materials and Methods<\/jats:title>\n                  <jats:p>We emphasize the synergy among diagnosis and treatment procedure in real-world TCM clinical settings to propose the PresRecST model, which effectively combines the key components of symptom collection, syndrome differentiation, treatment method determination, and herb recommendation. This model integrates a self-curated TCM knowledge graph to learn the high-quality representations of TCM biomedical entities and performs 3 stages of clinical predictions to meet the principle of systematic sequential procedure of TCM decision making.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Results<\/jats:title>\n                  <jats:p>To address the limitations of previous datasets, we constructed the TCM-Lung dataset, which is suitable for the simultaneous training of the syndrome differentiation, treatment method determination, and herb recommendation. Overall experimental results on 2 datasets demonstrate that the proposed PresRecST outperforms the state-of-the-art algorithm by significant improvements (eg, improvements of P@5 by 4.70%, P@10 by 5.37%, P@20 by 3.08% compared with the best baseline).<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Discussion<\/jats:title>\n                  <jats:p>The workflow of PresRecST effectively integrates the embedding vectors of the knowledge graph for progressive recommendation tasks, and it closely aligns with the actual diagnostic and treatment procedures followed by TCM doctors. A series of ablation experiments and case study show the availability and interpretability of PresRecST, indicating the proposed PresRecST can be beneficial for assisting the diagnosis and treatment in real-world TCM clinical settings.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Conclusion<\/jats:title>\n                  <jats:p>Our technology can be applied in a progressive recommendation scenario, providing recommendations for related items in a progressive manner, which can assist in providing more reliable diagnoses and herbal therapies for TCM clinical task.<\/jats:p>\n               <\/jats:sec>","DOI":"10.1093\/jamia\/ocae066","type":"journal-article","created":{"date-parts":[[2024,4,10]],"date-time":"2024-04-10T18:17:52Z","timestamp":1712773072000},"page":"1268-1279","source":"Crossref","is-referenced-by-count":37,"title":["PresRecST: a novel herbal prescription recommendation algorithm for real-world patients with integration of syndrome differentiation and treatment planning"],"prefix":"10.1093","volume":"31","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-1414-9354","authenticated-orcid":false,"given":"Xin","family":"Dong","sequence":"first","affiliation":[{"name":"Beijing Key Lab of Traffic Data Analysis and Mining, Institute of Medical Intelligence, School of Computer Science & Technology, Beijing Jiaotong University , Beijing 100044, China"}]},{"given":"Chenxi","family":"Zhao","sequence":"additional","affiliation":[{"name":"Beijing Key Lab of Traffic Data Analysis and Mining, Institute of Medical Intelligence, School of Computer Science & Technology, Beijing Jiaotong University , Beijing 100044, China"}]},{"given":"Xinpeng","family":"Song","sequence":"additional","affiliation":[{"name":"Beijing Key Lab of Traffic Data Analysis and Mining, Institute of Medical Intelligence, School of Computer Science & Technology, Beijing Jiaotong University , Beijing 100044, China"}]},{"given":"Lei","family":"Zhang","sequence":"additional","affiliation":[{"name":"National Data Center of Traditional Chinese Medicine, China Academy of Chinese Medical Sciences , Beijing 100700, China"}]},{"given":"Yu","family":"Liu","sequence":"additional","affiliation":[{"name":"Beijing Key Lab of Traffic Data Analysis and Mining, Institute of Medical Intelligence, School of Computer Science & Technology, Beijing Jiaotong University , Beijing 100044, China"}]},{"given":"Jun","family":"Wu","sequence":"additional","affiliation":[{"name":"Beijing Key Lab of Traffic Data Analysis and Mining, Institute of Medical Intelligence, School of Computer Science & Technology, Beijing Jiaotong University , Beijing 100044, China"}]},{"given":"Yiran","family":"Xu","sequence":"additional","affiliation":[{"name":"Department of Computer Science, Cornell University , New York, NY 14853, United States"}]},{"given":"Ning","family":"Xu","sequence":"additional","affiliation":[{"name":"National Data Center of Traditional Chinese Medicine, China Academy of Chinese Medical Sciences , Beijing 100700, China"}]},{"given":"Jialing","family":"Liu","sequence":"additional","affiliation":[{"name":"Beijing Key Lab of Traffic Data Analysis and Mining, Institute of Medical Intelligence, School of Computer Science & Technology, Beijing Jiaotong University , Beijing 100044, China"}]},{"given":"Haibin","family":"Yu","sequence":"additional","affiliation":[{"name":"The First Affiliated Hospital, Henan University of Chinese Medicine , Zhengzhou 450000, China"},{"name":"Collaborative Innovation Center for Chinese Medicine and Respiratory Diseases Co-Constructed by Henan Province & Education Ministry of P.R. 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