{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,2]],"date-time":"2026-01-02T02:09:33Z","timestamp":1767319773807,"version":"3.48.0"},"publisher-location":"Singapore","reference-count":45,"publisher":"Springer Nature Singapore","isbn-type":[{"value":"9789819538294","type":"print"},{"value":"9789819538300","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\/978-981-95-3830-0_4","type":"book-chapter","created":{"date-parts":[[2026,1,2]],"date-time":"2026-01-02T02:05:25Z","timestamp":1767319525000},"page":"51-66","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["From Static to\u00a0Dynamic: GNNs-Driven Clinical Decision-Making Assistance"],"prefix":"10.1007","author":[{"given":"Shiyi","family":"Lin","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zirui","family":"Zhuang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Qi","family":"Qi","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jingyu","family":"Wang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jianxin","family":"Liao","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jiachang","family":"Hao","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Haifeng","family":"Sun","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2026,1,2]]},"reference":[{"key":"4_CR1","unstructured":"Achiam, J., et\u00a0al.: GPT-4 technical report. arXiv preprint arXiv:2303.08774 (2023)"},{"key":"4_CR2","unstructured":"Bahdanau, D.: Neural machine translation by jointly learning to align and translate. arXiv preprint arXiv:1409.0473 (2014)"},{"issue":"1\u20137","key":"4_CR3","doi-asserted-by":"publisher","first-page":"107","DOI":"10.1016\/S0169-7552(98)00110-X","volume":"30","author":"S Brin","year":"1998","unstructured":"Brin, S., Page, L.: The anatomy of a large-scale hypertextual web search engine. Comput. Netw. ISDN Syst. 30(1\u20137), 107\u2013117 (1998)","journal-title":"Comput. Netw. ISDN Syst."},{"key":"4_CR4","doi-asserted-by":"crossref","unstructured":"Chen, D., Lin, Y., Li, W., Li, P., Zhou, J., Sun, X.: Measuring and relieving the over-smoothing problem for graph neural networks from the topological view. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol.\u00a034, pp. 3438\u20133445 (2020)","DOI":"10.1609\/aaai.v34i04.5747"},{"key":"4_CR5","doi-asserted-by":"crossref","unstructured":"Daigavane, A., Ravindran, B., Aggarwal, G.: Understanding convolutions on graphs. Distill 6(9), e32 (2021)","DOI":"10.23915\/distill.00032"},{"issue":"9","key":"4_CR6","doi-asserted-by":"publisher","first-page":"773","DOI":"10.1001\/amajethics.2020.773","volume":"22","author":"TS Doherty","year":"2020","unstructured":"Doherty, T.S., Carroll, A.E.: Believing in overcoming cognitive biases. AMA J. Ethics 22(9), 773\u2013778 (2020)","journal-title":"AMA J. Ethics"},{"key":"4_CR7","doi-asserted-by":"crossref","unstructured":"Feng, Y., et al.: Deep session interest network for click-through rate prediction. In: Proceedings of the 28th International Joint Conference on Artificial Intelligence, IJCAI 2019, pp. 2301\u20132307. AAAI Press (2019)","DOI":"10.24963\/ijcai.2019\/319"},{"key":"4_CR8","doi-asserted-by":"crossref","unstructured":"Grover, A., Leskovec, J.: node2vec: scalable feature learning for networks. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 855\u2013864 (2016)","DOI":"10.1145\/2939672.2939754"},{"key":"4_CR9","doi-asserted-by":"crossref","unstructured":"Guo, H., Tang, R., Ye, Y., Li, Z., He, X.: DeepFM: a factorization-machine based neural network for CTR prediction. In: Proceedings of the 26th International Joint Conference on Artificial Intelligence, IJCAI 2017, pp. 1725\u20131731. AAAI Press (2017)","DOI":"10.24963\/ijcai.2017\/239"},{"key":"4_CR10","unstructured":"Hamilton, W., Ying, Z., Leskovec, J.: Inductive representation learning on large graphs. Adv. Neural Inf. Process. Syst. 30 (2017)"},{"key":"4_CR11","doi-asserted-by":"publisher","unstructured":"He, X., Deng, K., Wang, X., Li, Y., Zhang, Y., Wang, M.: LightGCN: simplifying and powering graph convolution network for recommendation. In: Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR 2020, pp. 639\u2013648. Association for Computing Machinery, New York (2020). https:\/\/doi.org\/10.1145\/3397271.3401063","DOI":"10.1145\/3397271.3401063"},{"key":"4_CR12","doi-asserted-by":"publisher","unstructured":"He, X., Liao, L., Zhang, H., Nie, L., Hu, X., Chua, T.S.: Neural collaborative filtering. In: Proceedings of the 26th International Conference on World Wide Web, WWW 2017, pp. 173\u2013182. International World Wide Web Conferences Steering Committee, Republic and Canton of Geneva, CHE (2017). https:\/\/doi.org\/10.1145\/3038912.3052569","DOI":"10.1145\/3038912.3052569"},{"key":"4_CR13","doi-asserted-by":"crossref","unstructured":"Hou, Y., Hu, B., Zhang, Z., Zhao, W.X.: Core: simple and effective session-based recommendation within consistent representation space (2022). https:\/\/arxiv.org\/abs\/2204.11067","DOI":"10.1145\/3477495.3531955"},{"key":"4_CR14","doi-asserted-by":"publisher","unstructured":"Huang, P.S., He, X., Gao, J., Deng, L., Acero, A., Heck, L.: Learning deep structured semantic models for web search using clickthrough data. In: Proceedings of the 22nd ACM International Conference on Information & Knowledge Management, CIKM 2013, pp. 2333\u20132338. Association for Computing Machinery, New York (2013). https:\/\/doi.org\/10.1145\/2505515.2505665","DOI":"10.1145\/2505515.2505665"},{"key":"4_CR15","doi-asserted-by":"crossref","unstructured":"Johnson, A.E., et al.: MIMIC-IV, a freely accessible electronic health record dataset. Sci. Data 10(1), 1 (2023)","DOI":"10.1038\/s41597-022-01899-x"},{"key":"4_CR16","doi-asserted-by":"crossref","unstructured":"Johnson, A.E., et al.: MIMIC-III, a freely accessible critical care database. Sci. Data 3 (2016). https:\/\/doi.org\/10.1038\/sdata.2016.35","DOI":"10.1038\/sdata.2016.35"},{"key":"4_CR17","doi-asserted-by":"publisher","unstructured":"Kang, W.C., McAuley, J.: Self-attentive sequential recommendation. In: 2018 IEEE International Conference on Data Mining (ICDM), pp. 197\u2013206 (2018). https:\/\/doi.org\/10.1109\/ICDM.2018.00035","DOI":"10.1109\/ICDM.2018.00035"},{"issue":"1","key":"4_CR18","first-page":"2648","volume":"21","author":"SM Kazemi","year":"2020","unstructured":"Kazemi, S.M., et al.: Representation learning for dynamic graphs: a survey. J. Mach. Learn. Res. 21(1), 2648\u20132720 (2020)","journal-title":"J. Mach. Learn. Res."},{"key":"4_CR19","unstructured":"Kipf, T.N., Welling, M.: Semi-supervised classification with graph convolutional networks. arXiv preprint arXiv:1609.02907 (2016)"},{"key":"4_CR20","doi-asserted-by":"crossref","unstructured":"Krichene, W., Rendle, S.: On sampled metrics for item recommendation. In: Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 1748\u20131757 (2020)","DOI":"10.1145\/3394486.3403226"},{"key":"4_CR21","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":"4_CR22","unstructured":"Li, G., Xiong, C., Thabet, A., Ghanem, B.: DeeperGCN: all you need to train deeper GCNs (2020). https:\/\/arxiv.org\/abs\/2006.07739"},{"key":"4_CR23","unstructured":"Loshchilov, I., Hutter, F.: Decoupled weight decay regularization. arXiv preprint arXiv:1711.05101 (2017)"},{"key":"4_CR24","doi-asserted-by":"publisher","unstructured":"Mao, K., et al.: SimpleX: a simple and strong baseline for collaborative filtering. In: Proceedings of the 30th ACM International Conference on Information & Knowledge Management, CIKM 2021, pp. 1243\u20131252. Association for Computing Machinery, New York (2021). https:\/\/doi.org\/10.1145\/3459637.3482297","DOI":"10.1145\/3459637.3482297"},{"issue":"4","key":"4_CR25","doi-asserted-by":"publisher","first-page":"317","DOI":"10.7861\/clinmedicine.11-4-317","volume":"11","author":"G Neale","year":"2011","unstructured":"Neale, G., Hogan, H., Sevdalis, N.: Misdiagnosis: analysis based on case record review with proposals aimed to improve diagnostic processes. Clin. Med. 11(4), 317 (2011)","journal-title":"Clin. Med."},{"key":"4_CR26","doi-asserted-by":"crossref","unstructured":"Perozzi, B., Al-Rfou, R., Skiena, S.: DeepWalk: online learning of social representations. In: Proceedings of the 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 701\u2013710 (2014)","DOI":"10.1145\/2623330.2623732"},{"key":"4_CR27","doi-asserted-by":"publisher","unstructured":"Rendle, S.: Factorization machines. In: 2010 IEEE International Conference on Data Mining, pp. 995\u20131000 (2010). https:\/\/doi.org\/10.1109\/ICDM.2010.127","DOI":"10.1109\/ICDM.2010.127"},{"key":"4_CR28","doi-asserted-by":"crossref","unstructured":"Rombach, R., Blattmann, A., Lorenz, D., Esser, P., Ommer, B.: High-resolution image synthesis with latent diffusion models. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 10684\u201310695 (2022)","DOI":"10.1109\/CVPR52688.2022.01042"},{"key":"4_CR29","doi-asserted-by":"crossref","unstructured":"Sackett, D.L., Rosenberg, W.M., Gray, J.M., Haynes, R.B., Richardson, W.S.: Evidence based medicine: what it is and what it isn\u2019t (1996)","DOI":"10.1136\/bmj.312.7023.71"},{"key":"4_CR30","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.\u00a033, pp. 1126\u20131133 (2019)","DOI":"10.1609\/aaai.v33i01.33011126"},{"issue":"2","key":"4_CR31","doi-asserted-by":"publisher","first-page":"20","DOI":"10.1145\/2481244.2481248","volume":"14","author":"Y Sun","year":"2013","unstructured":"Sun, Y., Han, J.: Mining heterogeneous information networks: a structural analysis approach. ACM SIGKDD Exp. Newsl. 14(2), 20\u201328 (2013)","journal-title":"ACM SIGKDD Exp. Newsl."},{"key":"4_CR32","doi-asserted-by":"publisher","unstructured":"Tian, Z., Bai, T., Zhao, W.X., Wen, J.R., Cao, Z.: EulerNet: adaptive feature interaction learning via Euler\u2019s formula for CTR prediction. In: Proceedings of the 46th International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR 2023, pp. 1376\u20131385. Association for Computing Machinery, New York (2023). https:\/\/doi.org\/10.1145\/3539618.3591681","DOI":"10.1145\/3539618.3591681"},{"key":"4_CR33","unstructured":"Van Den\u00a0Berg, R., Thomas, N.K., Welling, M.: Graph convolutional matrix completion. arXiv preprint arXiv:1706.02263, 2(8), 9 (2017)"},{"key":"4_CR34","unstructured":"Veli\u010dkovi\u0107, P., Cucurull, G., Casanova, A., Romero, A., Lio, P., Bengio, Y.: Graph attention networks. arXiv preprint arXiv:1710.10903 (2017)"},{"key":"4_CR35","first-page":"1201","volume":"11","author":"SVN Vishwanathan","year":"2010","unstructured":"Vishwanathan, S.V.N., Schraudolph, N.N., Kondor, R., Borgwardt, K.M.: Graph kernels. J. Mach. Learn. Res. 11, 1201\u20131242 (2010)","journal-title":"J. Mach. Learn. Res."},{"key":"4_CR36","doi-asserted-by":"publisher","unstructured":"Wang, W., Xu, Y., Feng, F., Lin, X., He, X., Chua, T.S.: Diffusion recommender model. In: Proceedings of the 46th International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR 2023, pp. 832\u2013841. Association for Computing Machinery, New York (2023). https:\/\/doi.org\/10.1145\/3539618.3591663","DOI":"10.1145\/3539618.3591663"},{"key":"4_CR37","doi-asserted-by":"publisher","unstructured":"Wang, X., He, X., Wang, M., Feng, F., Chua, T.S.: Neural graph collaborative filtering. In: Proceedings of the 42nd International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR 2019, pp. 165\u2013174. Association for Computing Machinery, New York (2019). https:\/\/doi.org\/10.1145\/3331184.3331267","DOI":"10.1145\/3331184.3331267"},{"key":"4_CR38","doi-asserted-by":"publisher","unstructured":"Wu, J., et al.: Self-supervised graph learning for recommendation. In: Proceedings of the 44th International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR 2021, pp. 726\u2013735. Association for Computing Machinery, New York (2021). https:\/\/doi.org\/10.1145\/3404835.3462862","DOI":"10.1145\/3404835.3462862"},{"key":"4_CR39","doi-asserted-by":"crossref","unstructured":"Wu, R., Qiu, Z., Jiang, J., Qi, G., Wu, X.: Conditional generation net for medication recommendation. In: Proceedings of the ACM Web Conference 2022, pp. 935\u2013945 (2022)","DOI":"10.1145\/3485447.3511936"},{"key":"4_CR40","unstructured":"Xu, K., Hu, W., Leskovec, J., Jegelka, S.: How powerful are graph neural networks? arXiv preprint arXiv:1810.00826 (2018)"},{"key":"4_CR41","doi-asserted-by":"crossref","unstructured":"Xu, L., et al.: Towards a more user-friendly and easy-to-use benchmark library for recommender systems. In: SIGIR, pp. 2837\u20132847. ACM (2023)","DOI":"10.1145\/3539618.3591889"},{"key":"4_CR42","doi-asserted-by":"crossref","unstructured":"Yang, C., Xiao, C., Glass, L., Sun, J.: Change matters: medication change prediction with recurrent residual networks. In: Proceedings of the Thirtieth International Joint Conference on Artificial Intelligence, IJCAI 2021 (2021)","DOI":"10.24963\/ijcai.2021\/513"},{"key":"4_CR43","unstructured":"Yang, C., Xiao, C., Ma, F., Glass, L., Sun, J.: SafeDrug: dual molecular graph encoders for safe drug recommendations. In: Proceedings of the Thirtieth International Joint Conference on Artificial Intelligence, IJCAI 2021 (2021)"},{"key":"4_CR44","doi-asserted-by":"crossref","unstructured":"Zhou, G., et al.: Deep interest network for click-through rate prediction. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 1059\u20131068 (2018)","DOI":"10.1145\/3219819.3219823"},{"key":"4_CR45","doi-asserted-by":"publisher","unstructured":"Zhou, G., et al.: Deep interest network for click-through rate prediction. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, KDD 2018, pp. 1059\u20131068. Association for Computing Machinery, New York (2018). https:\/\/doi.org\/10.1145\/3219819.3219823","DOI":"10.1145\/3219819.3219823"}],"container-title":["Lecture Notes in Computer Science","Database Systems for Advanced Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-981-95-3830-0_4","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,1,2]],"date-time":"2026-01-02T02:05:29Z","timestamp":1767319529000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-981-95-3830-0_4"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026]]},"ISBN":["9789819538294","9789819538300"],"references-count":45,"URL":"https:\/\/doi.org\/10.1007\/978-981-95-3830-0_4","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 January 2026","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"DASFAA","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Database Systems for Advanced Applications","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Singapore","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Singapore","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":"26 May 2025","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"29 May 2025","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":"dasfaa2025","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/dasfaa2025.github.io","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}