{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,3]],"date-time":"2026-07-03T22:22:48Z","timestamp":1783117368615,"version":"3.54.6"},"publisher-location":"Singapore","reference-count":21,"publisher":"Springer Nature Singapore","isbn-type":[{"value":"9789819595747","type":"print"},{"value":"9789819595754","type":"electronic"}],"license":[{"start":{"date-parts":[[2026,1,1]],"date-time":"2026-01-01T00:00:00Z","timestamp":1767225600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2026,1,1]],"date-time":"2026-01-01T00:00:00Z","timestamp":1767225600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2026]]},"DOI":"10.1007\/978-981-95-9575-4_13","type":"book-chapter","created":{"date-parts":[[2026,7,3]],"date-time":"2026-07-03T22:02:25Z","timestamp":1783116145000},"page":"165-176","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Graph-Based Personalized Medication Recommendation Using EHR and\u00a0Drug Molecular Representations"],"prefix":"10.1007","author":[{"family":"Fangjing","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Quan","family":"Xuanrui","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xu","family":"Hongxia","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yan","family":"Jianzhuo","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2026,7,2]]},"reference":[{"issue":"3","key":"13_CR1","doi-asserted-by":"publisher","first-page":"216","DOI":"10.1093\/bfgp\/elac004","volume":"21","author":"ZH Ren","year":"2022","unstructured":"Ren, Z.H., Yu, C.Q., Li, L.P., et al.: BioDKG\u2013DDI: predicting drug\u2013drug interactions based on drug knowledge graph fusing biochemical information. Brief. Funct. Genomics 21(3), 216\u2013229 (2022)","journal-title":"Brief. Funct. Genomics"},{"issue":"6","key":"13_CR2","doi-asserted-by":"publisher","first-page":"bbab133","DOI":"10.1093\/bib\/bbab133","volume":"22","author":"AK Nyamabo","year":"2021","unstructured":"Nyamabo, A.K., Yu, H., Shi, J.Y.: SSI-DDI: substructure-substructure interactions for drug-drug interaction prediction. Brief. Bioinform. 22(6), bbab133 (2021)","journal-title":"Brief. Bioinform."},{"key":"13_CR3","unstructured":"Xia, J., et al.: Pre-training graph neural networks for molecular representations: retrospect and prospect. In: ICML 2022 2nd AI for Science Workshop (2022)"},{"issue":"1","key":"13_CR4","doi-asserted-by":"publisher","first-page":"18","DOI":"10.1038\/s41746-018-0029-1","volume":"1","author":"A Rajkomar","year":"2018","unstructured":"Rajkomar, A., Oren, E., Chen, K., et al.: Scalable and accurate deep learning with electronic health records. NPJ Digit. Med. 1(1), 18 (2018)","journal-title":"NPJ Digit. Med."},{"key":"13_CR5","unstructured":"Choi, E., et al.: RETAIN: an interpretable predictive model for healthcare using reverse time attention mechanism. In: Advances in Neural Information Processing Systems (NeurIPS), p. 29 (2016)"},{"key":"13_CR6","doi-asserted-by":"crossref","unstructured":"Choi, E., et al.: Multi-layer representation learning for medical concepts. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 1495\u20131504 (2016)","DOI":"10.1145\/2939672.2939823"},{"issue":"01","key":"13_CR7","first-page":"1126","volume":"33","author":"J Shang","year":"2019","unstructured":"Shang, J., et al.: GAMENet: graph augmented memory networks for recommending medication combination. Proc. AAAI Conf. Artif. Intell. 33(01), 1126\u20131133 (2019)","journal-title":"Proc. AAAI Conf. Artif. Intell."},{"key":"13_CR8","doi-asserted-by":"crossref","unstructured":"Yang, C., et al.: SafeDrug: dual molecular graph encoders for recommending effective and safe drug combinations (2021). arXiv preprint arXiv:2105.02711","DOI":"10.24963\/ijcai.2021\/514"},{"key":"13_CR9","unstructured":"Veli\u010dkovi\u0107, P., et al.: Graph Attention Networks (2017). arXiv preprint arXiv:1710.10903"},{"key":"13_CR10","unstructured":"Zhang, J., et al.: GAAN: gated attention networks for learning on large and spatiotemporal graphs (2018). arXiv preprint arXiv:1803.07294"},{"key":"13_CR11","doi-asserted-by":"crossref","unstructured":"Wang, X., et al.: KGAT: knowledge graph attention network for recommendation. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 950\u2013958 (2019)","DOI":"10.1145\/3292500.3330989"},{"issue":"1","key":"13_CR12","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1186\/s13321-020-00456-1","volume":"12","author":"AP Bento","year":"2020","unstructured":"Bento, A.P., Hersey, A., F\u00e9lix, E., et al.: An open source chemical structure curation pipeline using RDKit. J. Cheminformatics 12(1), 1\u201316 (2020)","journal-title":"J. Cheminformatics"},{"key":"13_CR13","doi-asserted-by":"crossref","unstructured":"Mao, S., et al.: Knowledge structure-aware graph-attention networks for knowledge tracing. In: International Conference on Knowledge Science, Engineering and Management, pp. 309\u2013321. Springer International Publishing, Cham (2022)","DOI":"10.1007\/978-3-031-10983-6_24"},{"key":"13_CR14","first-page":"547","volume":"37","author":"P Jaccard","year":"1901","unstructured":"Jaccard, P.: \u00c9tude comparative de la distribution florale dans une portion des Alpes et des Jura. Bulletin de la Soci\u00e9t\u00e9 Vaudoise des Sciences Naturelles 37, 547\u2013579 (1901)","journal-title":"Bulletin de la Soci\u00e9t\u00e9 Vaudoise des Sciences Naturelles"},{"issue":"6","key":"13_CR15","doi-asserted-by":"publisher","first-page":"1066","DOI":"10.1136\/amiajnl-2012-000935","volume":"19","author":"S Vilar","year":"2012","unstructured":"Vilar, S., Harpaz, R., Uriarte, E., et al.: Drug\u2013drug interaction through molecular structure similarity analysis. J. Am. Med. Inform. Assoc. 19(6), 1066\u20131074 (2012)","journal-title":"J. Am. Med. Inform. Assoc."},{"key":"13_CR16","doi-asserted-by":"crossref","unstructured":"Yang, N., Zeng, K., Wu, Q., Yan, J.: MoleRec: combinatorial drug recommendation with substructure-aware molecular representation learning. In: Proceedings of the ACM Web Conference 2023, pp. 4075\u20134085 (2023)","DOI":"10.1145\/3543507.3583872"},{"issue":"7","key":"13_CR17","first-page":"7053","volume":"37","author":"Q Chen","year":"2023","unstructured":"Chen, Q., Li, X., Geng, K., Wang, M.: Context-aware safe medication recommendations with molecular graph and DDI graph embedding. Proc. AAAI Conf. Artif. Intell. 37(7), 7053\u20137060 (2023)","journal-title":"Proc. AAAI Conf. Artif. Intell."},{"key":"13_CR18","doi-asserted-by":"publisher","DOI":"10.1016\/j.artmed.2023.102640","volume":"144","author":"Y Zhong","year":"2023","unstructured":"Zhong, Y., et al.: DDI-GCN: drug-drug interaction prediction via explainable graph convolutional networks. Artif. Intell. Med. 144, 102640 (2023)","journal-title":"Artif. Intell. Med."},{"issue":"4","key":"13_CR19","doi-asserted-by":"publisher","first-page":"1773","DOI":"10.1109\/JBHI.2024.3349570","volume":"28","author":"J Gao","year":"2024","unstructured":"Gao, J., Wu, Z., Al-Sabri, R., Oloulade, B.M., Chen, J.: AutoDDI: drug\u2013drug interaction prediction with automated graph neural network. IEEE J. Biomed. Health Inform. 28(4), 1773\u20131784 (2024)","journal-title":"IEEE J. Biomed. Health Inform."},{"issue":"7","key":"13_CR20","doi-asserted-by":"publisher","first-page":"2323","DOI":"10.1021\/acs.jcim.3c00771","volume":"64","author":"G Mqawass","year":"2024","unstructured":"Mqawass, G., Popov, P.: GraphLambda: fusion graph neural networks for binding affinity prediction. J. Chem. Inf. Model. 64(7), 2323\u20132330 (2024)","journal-title":"J. Chem. Inf. Model."},{"issue":"12","key":"13_CR21","doi-asserted-by":"publisher","first-page":"1023","DOI":"10.1038\/s43588-023-00558-4","volume":"3","author":"Y Zhang","year":"2023","unstructured":"Zhang, Y., et al.: Emerging drug interaction prediction enabled by flow-based graph neural network with biomedical network. Nat. Comput. Sci. 3(12), 1023\u20131033 (2023)","journal-title":"Nat. Comput. Sci."}],"container-title":["Lecture Notes in Computer Science","Brain Informatics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-981-95-9575-4_13","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,7,3]],"date-time":"2026-07-03T22:02:32Z","timestamp":1783116152000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-981-95-9575-4_13"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026]]},"ISBN":["9789819595747","9789819595754"],"references-count":21,"URL":"https:\/\/doi.org\/10.1007\/978-981-95-9575-4_13","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026]]},"assertion":[{"value":"2 July 2026","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"The authors have no competing interests.","order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Disclosure of Interests"}},{"value":"BI","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Brain Informatics","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Bari","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Italy","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2025","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"11 November 2025","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"13 November 2025","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"18","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"brain2025","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/wi-consortium.org\/conferences\/bi2025\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}