{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,27]],"date-time":"2025-03-27T10:45:29Z","timestamp":1743072329739,"version":"3.40.3"},"publisher-location":"Cham","reference-count":26,"publisher":"Springer Nature Switzerland","isbn-type":[{"type":"print","value":"9783031466700"},{"type":"electronic","value":"9783031466717"}],"license":[{"start":{"date-parts":[[2023,1,1]],"date-time":"2023-01-01T00:00:00Z","timestamp":1672531200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2023,1,1]],"date-time":"2023-01-01T00:00:00Z","timestamp":1672531200000},"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":[[2023]]},"DOI":"10.1007\/978-3-031-46671-7_3","type":"book-chapter","created":{"date-parts":[[2023,11,4]],"date-time":"2023-11-04T11:02:26Z","timestamp":1699095746000},"page":"33-47","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Graph Convolution Synthetic Transformer for\u00a0Chronic Kidney Disease Onset Prediction"],"prefix":"10.1007","author":[{"given":"Di","family":"Zhu","sequence":"first","affiliation":[]},{"given":"Yi","family":"Liu","sequence":"additional","affiliation":[]},{"given":"Weitong","family":"Chen","sequence":"additional","affiliation":[]},{"given":"Yanda","family":"Wang","sequence":"additional","affiliation":[]},{"given":"Yefan","family":"Huang","sequence":"additional","affiliation":[]},{"given":"Xiaoli","family":"Wang","sequence":"additional","affiliation":[]},{"given":"Ken","family":"Cai","sequence":"additional","affiliation":[]},{"given":"Bohan","family":"Li","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2023,11,5]]},"reference":[{"key":"3_CR1","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1146\/annurev-biodatasci-122320-112352","volume":"4","author":"L Bastarache","year":"2021","unstructured":"Bastarache, L.: Using phecodes for research with the electronic health record: from PheWAS to PheRS. Ann. Rev. Biomed. Data Sci. 4, 1\u201319 (2021)","journal-title":"Ann. Rev. Biomed. Data Sci."},{"doi-asserted-by":"crossref","unstructured":"Che, Z., Kale, D., Li, W., Bahadori, M.T., Liu, Y.: Deep computational phenotyping. In: Proceedings of the 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 507\u2013516 (2015)","key":"3_CR2","DOI":"10.1145\/2783258.2783365"},{"issue":"17","key":"3_CR3","doi-asserted-by":"publisher","first-page":"2651","DOI":"10.1093\/bioinformatics\/btab169","volume":"37","author":"Y Chen","year":"2021","unstructured":"Chen, Y., Ma, T., Yang, X., Wang, J., Song, B., Zeng, X.: Muffin: multi-scale feature fusion for drug-drug interaction prediction. Bioinformatics 37(17), 2651\u20132658 (2021)","journal-title":"Bioinformatics"},{"doi-asserted-by":"crossref","unstructured":"Choi, E., Bahadori, M.T., Song, L., Stewart, W.F., Sun, J.: Gram: graph-based attention model for healthcare representation learning. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 787\u2013795 (2017)","key":"3_CR4","DOI":"10.1145\/3097983.3098126"},{"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 29 (2016)","key":"3_CR5"},{"unstructured":"Choi, E., Xiao, C., Stewart, W., Sun, J.: Mime: multilevel medical embedding of electronic health records for predictive healthcare. In: Advances in Neural Information Processing Systems 31 (2018)","key":"3_CR6"},{"doi-asserted-by":"crossref","unstructured":"Choi, E., et al.: Learning the graphical structure of electronic health records with graph convolutional transformer. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 34, pp. 606\u2013613 (2020)","key":"3_CR7","DOI":"10.1609\/aaai.v34i01.5400"},{"doi-asserted-by":"crossref","unstructured":"Cui, L., Biswal, S., Glass, L.M., Lever, G., Sun, J., Xiao, C.: Conan: complementary pattern augmentation for rare disease detection. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 34, pp. 614\u2013621 (2020)","key":"3_CR8","DOI":"10.1609\/aaai.v34i01.5401"},{"key":"3_CR9","doi-asserted-by":"publisher","first-page":"211","DOI":"10.1016\/j.neucom.2018.10.028","volume":"325","author":"Y Ding","year":"2019","unstructured":"Ding, Y., Tang, J., Guo, F.: Identification of drug-side effect association via multiple information integration with centered kernel alignment. Neurocomputing 325, 211\u2013224 (2019)","journal-title":"Neurocomputing"},{"key":"3_CR10","first-page":"191","volume":"2020","author":"M Ghassemi","year":"2020","unstructured":"Ghassemi, M., Naumann, T., Schulam, P., Beam, A.L., Chen, I.Y., Ranganath, R.: A review of challenges and opportunities in machine learning for health. AMIA Summits Transl. Sci. Proc. 2020, 191 (2020)","journal-title":"AMIA Summits Transl. Sci. Proc."},{"doi-asserted-by":"crossref","unstructured":"Jagannatha, A.N., Yu, H.: Bidirectional RNN for medical event detection in electronic health records. In: Proceedings of the Conference. Association for Computational Linguistics. North American Chapter. Meeting, vol. 2016, p. 473. NIH Public Access (2016)","key":"3_CR11","DOI":"10.18653\/v1\/N16-1056"},{"issue":"10302","key":"3_CR12","doi-asserted-by":"publisher","first-page":"786","DOI":"10.1016\/S0140-6736(21)00519-5","volume":"398","author":"K Kalantar-Zadeh","year":"2021","unstructured":"Kalantar-Zadeh, K., Jafar, T.H., Nitsch, D., Neuen, B.L., Perkovic, V.: Chronic kidney disease. Lancet 398(10302), 786\u2013802 (2021)","journal-title":"Lancet"},{"issue":"1","key":"3_CR13","doi-asserted-by":"publisher","first-page":"71","DOI":"10.1007\/s41019-022-00176-6","volume":"7","author":"SR Khope","year":"2022","unstructured":"Khope, S.R., Elias, S.: Critical correlation of predictors for an efficient risk prediction framework of ICU patient using correlation and transformation of MIMIC-III dataset. Data Sci. Eng. 7(1), 71\u201386 (2022). https:\/\/doi.org\/10.1007\/s41019-022-00176-6","journal-title":"Data Sci. Eng."},{"issue":"2","key":"3_CR14","doi-asserted-by":"publisher","first-page":"1884","DOI":"10.1093\/bib\/bbaa040","volume":"22","author":"CY Lee","year":"2021","unstructured":"Lee, C.Y., Chen, Y.P.P.: Prediction of drug adverse events using deep learning in pharmaceutical discovery. Brief. Bioinform. 22(2), 1884\u20131901 (2021)","journal-title":"Brief. Bioinform."},{"doi-asserted-by":"crossref","unstructured":"Li, J., Wu, B., Sun, X., Wang, Y.: Causal hidden markov model for time series disease forecasting. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 12105\u201312114 (2021)","key":"3_CR15","DOI":"10.1109\/CVPR46437.2021.01193"},{"doi-asserted-by":"crossref","unstructured":"Luo, J., Ye, M., Xiao, C., Ma, F.: HitaNet: hierarchical time-aware attention networks for risk prediction on electronic health records. In: Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 647\u2013656 (2020)","key":"3_CR16","DOI":"10.1145\/3394486.3403107"},{"doi-asserted-by":"crossref","unstructured":"Ma, F., Chitta, R., Zhou, J., You, Q., Sun, T., Gao, J.: Dipole: diagnosis prediction in healthcare via attention-based bidirectional recurrent neural networks. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 1903\u20131911 (2017)","key":"3_CR17","DOI":"10.1145\/3097983.3098088"},{"doi-asserted-by":"crossref","unstructured":"Ma, F., Gao, J., Suo, Q., You, Q., Zhou, J., Zhang, A.: Risk prediction on electronic health records with prior medical knowledge. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 1910\u20131919 (2018)","key":"3_CR18","DOI":"10.1145\/3219819.3220020"},{"issue":"1","key":"3_CR19","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1038\/srep26094","volume":"6","author":"R Miotto","year":"2016","unstructured":"Miotto, R., Li, L., Kidd, B.A., Dudley, J.T.: Deep patient: an unsupervised representation to predict the future of patients from the electronic health records. Sci. Rep. 6(1), 1\u201310 (2016)","journal-title":"Sci. Rep."},{"doi-asserted-by":"crossref","unstructured":"Peng, X., Long, G., Shen, T., Wang, S., Jiang, J.: Sequential diagnosis prediction with transformer and ontological representation. In: 2021 IEEE International Conference on Data Mining (ICDM), pp. 489\u2013498. IEEE (2021)","key":"3_CR20","DOI":"10.1109\/ICDM51629.2021.00060"},{"key":"3_CR21","doi-asserted-by":"publisher","first-page":"30","DOI":"10.1007\/978-3-319-31750-2_3","volume-title":"Advances in Knowledge Discovery and Data Mining: 20th Pacific-Asia Conference, PAKDD 2016, Auckland, New Zealand, April 19-22, 2016, Proceedings, Part II","author":"T Pham","year":"2016","unstructured":"Pham, T., Tran, T., Phung, D., Venkatesh, S.: DeepCare: a deep dynamic memory model for\u00a0predictive medicine. In: Bailey, J., Khan, L., Washio, T., Dobbie, G., Huang, J.Z., Wang, R. (eds.) Advances in Knowledge Discovery and Data Mining: 20th Pacific-Asia Conference, PAKDD 2016, Auckland, New Zealand, April 19-22, 2016, Proceedings, Part II, pp. 30\u201341. Springer, Cham (2016). https:\/\/doi.org\/10.1007\/978-3-319-31750-2_3"},{"doi-asserted-by":"crossref","unstructured":"Ramchoun, H., Ghanou, Y., Ettaouil, M., Janati Idrissi, M.A.: Multilayer perceptron: architecture optimization and training (2016)","key":"3_CR22","DOI":"10.1145\/3090354.3090427"},{"doi-asserted-by":"crossref","unstructured":"Seber, G.A., Lee, A.J.: Linear Regression Analysis, vol. 330. John Wiley & Sons (2003)","key":"3_CR23","DOI":"10.1002\/9780471722199"},{"doi-asserted-by":"crossref","unstructured":"Shang, J., Ma, T., Xiao, C., Sun, J.: Pre-training of graph augmented transformers for medication recommendation. arXiv preprint arXiv:1906.00346 (2019)","key":"3_CR24","DOI":"10.24963\/ijcai.2019\/825"},{"key":"3_CR25","doi-asserted-by":"publisher","DOI":"10.1016\/j.jbi.2020.103671","volume":"115","author":"Y Si","year":"2021","unstructured":"Si, Y., et al.: Deep representation learning of patient data from electronic health records (EHR): a systematic review. J. Biomed. Inform. 115, 103671 (2021)","journal-title":"J. Biomed. Inform."},{"issue":"2","key":"3_CR26","doi-asserted-by":"publisher","first-page":"85","DOI":"10.1007\/s41019-023-00209-8","volume":"8","author":"D Zhu","year":"2023","unstructured":"Zhu, D.: A survey of advanced information fusion system: from model-driven to knowledge-enabled. Data Sci. Eng. 8(2), 85\u201397 (2023). https:\/\/doi.org\/10.1007\/s41019-023-00209-8","journal-title":"Data Sci. Eng."}],"container-title":["Lecture Notes in Computer Science","Advanced Data Mining and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-46671-7_3","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,11,4]],"date-time":"2023-11-04T11:02:51Z","timestamp":1699095771000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-46671-7_3"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023]]},"ISBN":["9783031466700","9783031466717"],"references-count":26,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-46671-7_3","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2023]]},"assertion":[{"value":"5 November 2023","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ADMA","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Advanced Data Mining and Applications","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Shenyang","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":"2023","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"27 August 2023","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"29 August 2023","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"9","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"adma2023","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/adma2023.uqcloud.net\/","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":"Yes. Microsoft CMT","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"503","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":"216","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":"43% - 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":"2.97","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":"3.77","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":"No","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}}]}}