{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,27]],"date-time":"2025-03-27T07:23:10Z","timestamp":1743060190316,"version":"3.40.3"},"publisher-location":"Cham","reference-count":17,"publisher":"Springer Nature Switzerland","isbn-type":[{"type":"print","value":"9783031204999"},{"type":"electronic","value":"9783031205002"}],"license":[{"start":{"date-parts":[[2022,1,1]],"date-time":"2022-01-01T00:00:00Z","timestamp":1640995200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2022,1,1]],"date-time":"2022-01-01T00:00:00Z","timestamp":1640995200000},"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":[[2022]]},"DOI":"10.1007\/978-3-031-20500-2_18","type":"book-chapter","created":{"date-parts":[[2023,1,1]],"date-time":"2023-01-01T05:12:32Z","timestamp":1672549952000},"page":"218-229","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Interaction-Aware Temporal Prescription Generation via\u00a0Message Passing Neural Network"],"prefix":"10.1007","author":[{"given":"Cong","family":"Wang","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zhi","family":"Zheng","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Tong","family":"Xu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zikai","family":"Yin","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Enhong","family":"Chen","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2023,1,1]]},"reference":[{"issue":"8","key":"18_CR1","doi-asserted-by":"publisher","first-page":"3679","DOI":"10.1002\/mp.13597","volume":"46","author":"AM Barrag\u00e1n-Montero","year":"2019","unstructured":"Barrag\u00e1n-Montero, A.M.: Three-dimensional dose prediction for lung IMRT patients with deep neural networks: robust learning from heterogeneous beam configurations. Med. Phys. 46(8), 3679\u20133691 (2019)","journal-title":"Med. Phys."},{"key":"18_CR2","doi-asserted-by":"crossref","unstructured":"Baytas, I.M., Xiao, C., Zhang, X., Wang, F., Jain, A.K., Zhou, J.: Patient subtyping via time-aware lstm networks. In: Proceedings of the 23rd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 65\u201374 (2017)","DOI":"10.1145\/3097983.3097997"},{"key":"18_CR3","doi-asserted-by":"crossref","unstructured":"Chen, L., Liu, Y., He, X., Gao, L., Zheng, Z.: Matching user with item set: collaborative bundle recommendation with deep attention network. In: IJCAI, pp. 2095\u20132101 (2019)","DOI":"10.24963\/ijcai.2019\/290"},{"key":"18_CR4","unstructured":"Choi, E., Bahadori, M.T., Schuetz, A., Stewart, W.F., Sun, J.: Doctor ai: predicting clinical events via recurrent neural networks. In: Machine Learning for Healthcare Conference, pp. 301\u2013318. PMLR (2016)"},{"key":"18_CR5","unstructured":"Choi, E., Bahadori, M.T., Sun, J., Kulas, J., Schuetz, A., Stewart, W.: Retain: an interpretable predictive model for healthcare using reverse time attention mechanism. In: Advances in Neural Information Processing Systems, vol. 29 (2016)"},{"key":"18_CR6","unstructured":"Gilmer, J., Schoenholz, S.S., Riley, P.F., Vinyals, O., Dahl, G.E.: Neural message passing for quantum chemistry. In: International Conference on Machine Learning, pp. 1263\u20131272. PMLR (2017)"},{"key":"18_CR7","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., Sun, J.: Delving deep into rectifiers: surpassing human-level performance on imagenet classification. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 1026\u20131034 (2015)","DOI":"10.1109\/ICCV.2015.123"},{"key":"18_CR8","doi-asserted-by":"crossref","unstructured":"Jin, B., Yang, H., Sun, L., Liu, C., Qu, Y., Tong, J.: A treatment engine by predicting next-period prescriptions. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 1608\u20131616 (2018)","DOI":"10.1145\/3219819.3220095"},{"key":"18_CR9","doi-asserted-by":"crossref","unstructured":"Le, H., Tran, T., Venkatesh, S.: Dual memory neural computer for asynchronous two-view sequential learning. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 1637\u20131645 (2018)","DOI":"10.1145\/3219819.3219981"},{"key":"18_CR10","unstructured":"Lipton, Z.C., Kale, D.C., Elkan, C., Wetzel, R.: Learning to diagnose with LSTM recurrent neural networks. arXiv preprint. arXiv:1511.03677 (2015)"},{"issue":"12","key":"18_CR11","doi-asserted-by":"publisher","first-page":"2849","DOI":"10.1007\/s13042-020-01155-x","volume":"11","author":"S Liu","year":"2020","unstructured":"Liu, S., et al.: A hybrid method of recurrent neural network and graph neural network for next-period prescription prediction. Int. J. Mach. Learn. Cybern. 11(12), 2849\u20132856 (2020)","journal-title":"Int. J. Mach. Learn. Cybern."},{"key":"18_CR12","doi-asserted-by":"publisher","first-page":"752007","DOI":"10.3389\/fonc.2021.752007","volume":"11","author":"Y Liu","year":"2021","unstructured":"Liu, Y., et al.: Dose prediction using a three-dimensional convolutional neural network for nasopharyngeal carcinoma with tomotherapy. Front. Oncol. 11, 752007\u2013752007 (2021)","journal-title":"Front. Oncol."},{"key":"18_CR13","doi-asserted-by":"crossref","unstructured":"Shang, J., Xiao, C., Ma, T., Li, H., Sun, J.: Gamenet: graph augmented memory networks for recommending medication combination. In: proceedings of the AAAI Conference on Artificial Intelligence, vol. 33, pp. 1126\u20131133 (2019)","DOI":"10.1609\/aaai.v33i01.33011126"},{"key":"18_CR14","unstructured":"Yang, P., Sun, X., Li, W., Ma, S., Wu, W., Wang, H.: SGM: sequence generation model for multi-label classification. arXiv preprint. arXiv:1806.04822 (2018)"},{"key":"18_CR15","doi-asserted-by":"crossref","unstructured":"Zhang, Y., Chen, R., Tang, J., Stewart, W.F., Sun, J.: Leap: learning to prescribe effective and safe treatment combinations for multimorbidity. In: proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and data Mining, pp. 1315\u20131324 (2017)","DOI":"10.1145\/3097983.3098109"},{"key":"18_CR16","doi-asserted-by":"crossref","unstructured":"Zheng, Z., et al.: Drug package recommendation via interaction-aware graph induction. In: Proceedings of the Web Conference 2021, pp. 1284\u20131295 (2021)","DOI":"10.1145\/3442381.3449962"},{"key":"18_CR17","doi-asserted-by":"crossref","unstructured":"Zheng, Z., et al.: Interaction-aware drug package recommendation via policy gradient. In: ACM Transactions on Information Systems (TOIS) (2022)","DOI":"10.1145\/3511020"}],"container-title":["Lecture Notes in Computer Science","Artificial Intelligence"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-20500-2_18","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,1,1]],"date-time":"2023-01-01T05:33:37Z","timestamp":1672551217000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-20500-2_18"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022]]},"ISBN":["9783031204999","9783031205002"],"references-count":17,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-20500-2_18","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2022]]},"assertion":[{"value":"1 January 2023","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"CICAI","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"CAAI International Conference on Artificial Intelligence","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Beijing","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"China","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2022","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"27 August 2022","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"28 August 2022","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"cicai2022","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/cicai.caai.cn\/#\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Double-blind","order":1,"name":"type","label":"Type","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"CMT","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"472","order":3,"name":"number_of_submissions_sent_for_review","label":"Number of Submissions Sent for Review","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"164","order":4,"name":"number_of_full_papers_accepted","label":"Number of Full Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"0","order":5,"name":"number_of_short_papers_accepted","label":"Number of Short Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"35% - The value is computed by the equation \"Number of Full Papers Accepted \/ Number of Submissions Sent for Review * 100\" and then rounded to a whole number.","order":6,"name":"acceptance_rate_of_full_papers","label":"Acceptance Rate of Full Papers","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"3.1","order":7,"name":"average_number_of_reviews_per_paper","label":"Average Number of Reviews per Paper","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"3.7","order":8,"name":"average_number_of_papers_per_reviewer","label":"Average Number of Papers per Reviewer","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"Yes","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}}]}}