{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,12,3]],"date-time":"2025-12-03T18:14:29Z","timestamp":1764785669111,"version":"3.43.0"},"reference-count":0,"publisher":"IOS Press","isbn-type":[{"value":"9781643686080","type":"electronic"}],"license":[{"start":{"date-parts":[[2025,8,7]],"date-time":"2025-08-07T00:00:00Z","timestamp":1754524800000},"content-version":"unspecified","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2025,8,7]]},"abstract":"<jats:p>Rheumatoid arthritis (RA) is an incurable chronic disease with low remission and response rates, making effective drug selection essential. We developed a hierarchical drug recommendation system enhanced by a graph embedding module. By integrating patient feature representations predicted through model probabilities with graph embeddings from EHR and DDI matrices processed by a dual-layer Graph Convolutional Network(GCN), the model predicts drug classes and further refines predictions with subclass information. Using data collected from 352 hospitals across China between 2000 and 2023, we established a cohort of 4975 single-diagnosis RA patients who met the remission criteria. Eight subcategories from four first-line RA drug classes, including glucocorticoids, immunosuppressants, specialized immunosuppressants, and NSAIDs, were selected as recommendation labels. Our model outperforms existing methods, improving F1 and AUC scores by 9% and 19%, respectively, and demonstrates strong potential for personalizing RA medication recommendations.<\/jats:p>","DOI":"10.3233\/shti251016","type":"book-chapter","created":{"date-parts":[[2025,8,7]],"date-time":"2025-08-07T11:38:15Z","timestamp":1754566695000},"source":"Crossref","is-referenced-by-count":1,"title":["Graph-Embedding Enhanced Hierarchical Model for Personalized Rheumatoid Arthritis Drug Recommendations"],"prefix":"10.3233","author":[{"given":"Shuyu","family":"Ouyang","sequence":"first","affiliation":[{"name":"College of Biomedical Engineering and Instrument Science, Zhejiang University, Engineering Research Center of EMR and Intelligent Expert System, Ministry of Education, Hangzhou 310027, China"}]},{"given":"Yu","family":"Tian","sequence":"additional","affiliation":[{"name":"College of Biomedical Engineering and Instrument Science, Zhejiang University, Engineering Research Center of EMR and Intelligent Expert System, Ministry of Education, Hangzhou 310027, China"}]},{"given":"Dubai","family":"Li","sequence":"additional","affiliation":[{"name":"College of Biomedical Engineering and Instrument Science, Zhejiang University, Engineering Research Center of EMR and Intelligent Expert System, Ministry of Education, Hangzhou 310027, China"}]},{"given":"Huayu","family":"Yu","sequence":"additional","affiliation":[{"name":"College of Biomedical Engineering and Instrument Science, Zhejiang University, Engineering Research Center of EMR and Intelligent Expert System, Ministry of Education, Hangzhou 310027, China"}]},{"given":"Danyang","family":"Tong","sequence":"additional","affiliation":[{"name":"Research Center for Data Hub and Security, Zhejiang Lab, Hangzhou 311121, China"}]},{"given":"Tianshu","family":"Zhou","sequence":"additional","affiliation":[{"name":"Research Center for Data Hub and Security, Zhejiang Lab, Hangzhou 311121, China"}]},{"given":"Nan","family":"Jiang","sequence":"additional","affiliation":[{"name":"Department of Rheumatology and Clinical Immunology, Peking Union Medical College Hospital (PUMCH), Chinese Academy of Medical Sciences and Peking Union Medical College, National Clinical Research Center for Dermatologic and Immunologic Diseases (NCRC-DID), Ministry of Science and Technology, State Key Laboratory of Complex Severe and Rare Diseases, Key Laboratory of Rheumatology and Clinical Immunology, Ministry of Education, Beijing 100730, China"}]},{"given":"Qian","family":"Wang","sequence":"additional","affiliation":[{"name":"Department of Rheumatology and Clinical Immunology, Peking Union Medical College Hospital (PUMCH), Chinese Academy of Medical Sciences and Peking Union Medical College, National Clinical Research Center for Dermatologic and Immunologic Diseases (NCRC-DID), Ministry of Science and Technology, State Key Laboratory of Complex Severe and Rare Diseases, Key Laboratory of Rheumatology and Clinical Immunology, Ministry of Education, Beijing 100730, China"}]},{"given":"Xinping","family":"Tian","sequence":"additional","affiliation":[{"name":"Department of Rheumatology and Clinical Immunology, Peking Union Medical College Hospital (PUMCH), Chinese Academy of Medical Sciences and Peking Union Medical College, National Clinical Research Center for Dermatologic and Immunologic Diseases (NCRC-DID), Ministry of Science and Technology, State Key Laboratory of Complex Severe and Rare Diseases, Key Laboratory of Rheumatology and Clinical Immunology, Ministry of Education, Beijing 100730, China"}]},{"given":"Jingsong","family":"Li","sequence":"additional","affiliation":[{"name":"College of Biomedical Engineering and Instrument Science, Zhejiang University, Engineering Research Center of EMR and Intelligent Expert System, Ministry of Education, Hangzhou 310027, China"},{"name":"Research Center for Data Hub and Security, Zhejiang Lab, Hangzhou 311121, China"}]}],"member":"7437","container-title":["Studies in Health Technology and Informatics","MEDINFO 2025 \u2014 Healthcare Smart \u00d7 Medicine Deep"],"original-title":[],"link":[{"URL":"https:\/\/ebooks.iospress.nl\/pdf\/doi\/10.3233\/SHTI251016","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,8,7]],"date-time":"2025-08-07T11:38:15Z","timestamp":1754566695000},"score":1,"resource":{"primary":{"URL":"https:\/\/ebooks.iospress.nl\/doi\/10.3233\/SHTI251016"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,8,7]]},"ISBN":["9781643686080"],"references-count":0,"URL":"https:\/\/doi.org\/10.3233\/shti251016","relation":{},"ISSN":["0926-9630","1879-8365"],"issn-type":[{"value":"0926-9630","type":"print"},{"value":"1879-8365","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,8,7]]}}}