{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,8]],"date-time":"2026-06-08T11:08:52Z","timestamp":1780916932084,"version":"3.54.1"},"publisher-location":"Singapore","reference-count":24,"publisher":"Springer Nature Singapore","isbn-type":[{"value":"9789819214648","type":"print"},{"value":"9789819214655","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\/978-981-92-1465-5_22","type":"book-chapter","created":{"date-parts":[[2026,6,8]],"date-time":"2026-06-08T10:24:21Z","timestamp":1780914261000},"page":"278-290","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["MetaGD-CAN: A Hybrid Generative\u2013Discriminative Method for\u00a0Cancer Detection in\u00a0EHR Data"],"prefix":"10.1007","author":[{"given":"Yu-Hsiang","family":"Chang","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Wei-Chun","family":"Tsai","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Lo Pang-Yun","family":"Ting","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Kun-Ta","family":"Chuang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2026,6,9]]},"reference":[{"key":"22_CR1","doi-asserted-by":"crossref","unstructured":"Adhikari, B., Zhang, Y., Ramakrishnan, N., Prakash, B.A.: Sub2vec: feature learning for subgraphs. In: Pacific-Asia Conference on Knowledge Discovery and Data Mining, pp. 170\u2013182 (2018)","DOI":"10.1007\/978-3-319-93037-4_14"},{"issue":"11","key":"22_CR2","doi-asserted-by":"publisher","first-page":"1959","DOI":"10.1038\/sj.bjc.6602587","volume":"92","author":"V Allgar","year":"2005","unstructured":"Allgar, V., Neal, R.: Delays in the diagnosis of six cancers: analysis of data from the national survey of NHS patients: cancer. Br. J. Cancer 92(11), 1959\u20131970 (2005)","journal-title":"Br. J. Cancer"},{"key":"22_CR3","doi-asserted-by":"crossref","unstructured":"Che, Z., Purushotham, S., Cho, K., Sontag, D.A., Liu, Y.: Recurrent neural networks for multivariate time series with missing values. Sci. Rep. 8 (2018)","DOI":"10.1038\/s41598-018-24271-9"},{"key":"22_CR4","unstructured":"Chen, J., Yin, C., Wang, Y., Zhang, P.: Predictive modeling with temporal graphical representation on electronic health records. In: IJCAI: Proceedings of the Conference, vol.\u00a02024, p.\u00a05763 (2024)"},{"key":"22_CR5","doi-asserted-by":"crossref","unstructured":"Chen, T., Guestrin, C.: XGBoost: a scalable tree boosting system. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (2016)","DOI":"10.1145\/2939672.2939785"},{"key":"22_CR6","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)"},{"issue":"11","key":"22_CR7","doi-asserted-by":"publisher","first-page":"665","DOI":"10.1038\/s42256-020-00257-z","volume":"2","author":"R Geirhos","year":"2020","unstructured":"Geirhos, R., et al.: Shortcut learning in deep neural networks. Nat. Mach. Intell. 2(11), 665\u2013673 (2020)","journal-title":"Nat. Mach. Intell."},{"issue":"6","key":"22_CR8","doi-asserted-by":"publisher","first-page":"659","DOI":"10.18553\/jmcp.2023.29.6.659","volume":"29","author":"M Gitlin","year":"2023","unstructured":"Gitlin, M., McGarvey, N., Shivaprakash, N., Cong, Z.: Time duration and health care resource use during cancer diagnoses in the united states: a large claims database analysis. J. Managed Care Specialty Pharmacy 29(6), 659\u2013670 (2023)","journal-title":"J. Managed Care Specialty Pharmacy"},{"key":"22_CR9","doi-asserted-by":"crossref","unstructured":"Goyal, N., Jain, H.V., Ranu, S.: GraphGen: a scalable approach to domain-agnostic labeled graph generation. In: Proceedings of the Web Conference 2020, pp. 1253\u20131263 (2020)","DOI":"10.1145\/3366423.3380201"},{"key":"22_CR10","doi-asserted-by":"crossref","unstructured":"Heppner, G.H., Shapiro, W.R., Rankin, J.K.: Tumor heterogeneity. Pediatric Oncology 1: with a special section on Rare Primitive Neuroectodermal Tumors, pp. 99\u2013116 (1981)","DOI":"10.1007\/978-94-009-8219-2_4"},{"key":"22_CR11","unstructured":"Jiang, P., Xiao, C., Cross, A.R., Sun, J.: GraphCare: enhancing healthcare predictions with personalized knowledge graphs. In: The Twelfth International Conference on Learning Representations (2024)"},{"key":"22_CR12","unstructured":"Johnson, A., et al.: MIMIC-IV (version 3.0). physionet (2024). RRID:SCR_007345"},{"issue":"1","key":"22_CR13","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1038\/s41597-022-01899-x","volume":"10","author":"AE Johnson","year":"2023","unstructured":"Johnson, A.E., et al.: MIMIC-IV, a freely accessible electronic health record dataset. Sci. Data 10(1), 1 (2023)","journal-title":"Sci. Data"},{"issue":"3366","key":"22_CR14","doi-asserted-by":"publisher","first-page":"9","DOI":"10.1126\/science.130.3366.9","volume":"130","author":"RS Ledley","year":"1959","unstructured":"Ledley, R.S., Lusted, L.B.: Reasoning foundations of medical diagnosis: symbolic logic, probability, and value theory aid our understanding of how physicians reason. Science 130(3366), 9\u201321 (1959)","journal-title":"Science"},{"key":"22_CR15","unstructured":"Li, A.C., Kumar, A., Pathak, D.: Generative classifiers avoid shortcut solutions. In: The Thirteenth International Conference on Learning Representations (2025)"},{"key":"22_CR16","doi-asserted-by":"crossref","unstructured":"McDuff, D., et\u00a0al.: Towards accurate differential diagnosis with large language models. Nature, pp. 451\u2013457 (2025)","DOI":"10.1038\/s41586-025-08869-4"},{"key":"22_CR17","unstructured":"Ng, A., Jordan, M.: On discriminative vs. generative classifiers: a comparison of logistic regression and naive bayes. In: Advances in Neural Information Processing Systems, vol. 14 (2001)"},{"key":"22_CR18","unstructured":"Poulain, R., Beheshti, R.: Graph transformers on EHRs: better representation improves downstream performance. In: The Twelfth International Conference on Learning Representations (2024)"},{"key":"22_CR19","doi-asserted-by":"crossref","unstructured":"Rasmy, L., Xiang, Y., Xie, Z., Tao, C., Zhi, D.: Med-BERT: pretrained contextualized embeddings on large-scale structured electronic health records for disease prediction. NPJ Digit. Med. 4 (2021)","DOI":"10.1038\/s41746-021-00455-y"},{"key":"22_CR20","unstructured":"Teru, K., Denis, E., Hamilton, W.: Inductive relation prediction by subgraph reasoning. In: International Conference on Machine Learning, pp. 9448\u20139457 (2020)"},{"key":"22_CR21","doi-asserted-by":"crossref","unstructured":"Ting, L.P., Chen, H., Liu, A., Yeh, C., Chen, P., Chuang, K.: Early detection of patient deterioration from real-time wearable monitoring system. In: Proceedings of the Thirty-Fourth International Joint Conference on Artificial Intelligence, IJCAI 2025, pp. 9871\u20139879 (2025)","DOI":"10.24963\/ijcai.2025\/1097"},{"key":"22_CR22","doi-asserted-by":"crossref","unstructured":"Wen, Q., Ouyang, Z., Zhang, J., Qian, Y., Ye, Y., Zhang, C.: Disentangled dynamic heterogeneous graph learning for opioid overdose prediction. In: Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, pp. 2009\u20132019 (2022)","DOI":"10.1145\/3534678.3539279"},{"key":"22_CR23","first-page":"582","volume":"2023","author":"R Xu","year":"2023","unstructured":"Xu, R., Ali, M.K., Ho, J.C., Yang, C.: Hypergraph transformers for EHR-based clinical predictions. AMIA Summits Transl. Sci. Proc. 2023, 582 (2023)","journal-title":"AMIA Summits Transl. Sci. Proc."},{"key":"22_CR24","unstructured":"Yan, X., Han, J.: GSPAN: graph-based substructure pattern mining. In: 2002 IEEE International Conference on Data Mining Proceedings, pp. 721\u2013724 (2002)"}],"container-title":["Lecture Notes in Computer Science","Advances in Knowledge Discovery and Data Mining"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-981-92-1465-5_22","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,6,8]],"date-time":"2026-06-08T10:24:34Z","timestamp":1780914274000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-981-92-1465-5_22"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026]]},"ISBN":["9789819214648","9789819214655"],"references-count":24,"URL":"https:\/\/doi.org\/10.1007\/978-981-92-1465-5_22","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":"9 June 2026","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"PAKDD","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Pacific-Asia Conference on Knowledge Discovery and Data Mining","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Hong Kong","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":"2026","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"9 June 2026","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"12 June 2026","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"30","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"pakdd2026","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/www.pakdd2026.org\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}