{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,26]],"date-time":"2025-03-26T17:51:42Z","timestamp":1743011502548,"version":"3.40.3"},"publisher-location":"Singapore","reference-count":25,"publisher":"Springer Nature Singapore","isbn-type":[{"type":"print","value":"9789811992964"},{"type":"electronic","value":"9789811992971"}],"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\/978-981-19-9297-1_23","type":"book-chapter","created":{"date-parts":[[2023,1,19]],"date-time":"2023-01-19T10:33:02Z","timestamp":1674124382000},"page":"320-332","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Multi-Cause Learning for\u00a0Diagnosis Prediction"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-3824-0806","authenticated-orcid":false,"given":"Liping","family":"Wang","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9233-3827","authenticated-orcid":false,"given":"Qiang","family":"Liu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7151-9550","authenticated-orcid":false,"given":"Huanhuan","family":"Ma","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2164-3577","authenticated-orcid":false,"given":"Shu","family":"Wu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5224-8647","authenticated-orcid":false,"given":"Liang","family":"Wang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2023,1,20]]},"reference":[{"key":"23_CR1","doi-asserted-by":"crossref","unstructured":"Cen, Y., Zhang, J., Zou, X., Zhou, C., Yang, H., Tang, J.: Controllable multi-interest framework for recommendation. In: Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining (2020)","DOI":"10.1145\/3394486.3403344"},{"key":"23_CR2","unstructured":"Choi, E., Bahadori, M.T., Kulas, J.A., Schuetz, A., Stewart, W.F., Sun, J.: Retain: an interpretable predictive model for healthcare using reverse time attention mechanism. In: Proceedings of the 30th International Conference on Neural Information Processing Systems (2016)"},{"key":"23_CR3","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 (2017)","DOI":"10.1145\/3097983.3098126"},{"key":"23_CR4","doi-asserted-by":"crossref","unstructured":"Ernst, P., Meng, C., Siu, A., Weikum, G.: KnowLife: a knowledge graph for health and life sciences. In: 2014 IEEE 30th International Conference on Data Engineering (2014)","DOI":"10.1109\/ICDE.2014.6816754"},{"key":"23_CR5","doi-asserted-by":"crossref","unstructured":"Gao, J., Xiao, C., Wang, Y., Tang, W., Glass, L.M., Sun, J.: Stagenet: stage-aware neural networks for health risk prediction. In: Proceedings of The Web Conference 2020 (2020)","DOI":"10.1145\/3366423.3380136"},{"key":"23_CR6","doi-asserted-by":"crossref","unstructured":"Gretton, A., Bousquet, O., Smola, A., Sch\u00f6lkopf, B.: Measuring statistical dependence with Hilbert-Schmidt norms. In: Algorithmic Learning Theory (2005)","DOI":"10.1007\/11564089_7"},{"key":"23_CR7","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: 2016 IEEE Conference on Computer Vision and Pattern Recognition (2016)","DOI":"10.1109\/CVPR.2016.90"},{"key":"23_CR8","doi-asserted-by":"publisher","first-page":"1735","DOI":"10.1162\/neco.1997.9.8.1735","volume":"9","author":"S Hochreiter","year":"1997","unstructured":"Hochreiter, S., Schmidhuber, J.: Long short-term memory. Neural Comput. 9, 1735\u20131780 (1997)","journal-title":"Neural Comput."},{"key":"23_CR9","unstructured":"Kingma, D.P., Ba, J.: Adam: a method for stochastic optimization. In: International Conference on Learning Representations (2015)"},{"key":"23_CR10","doi-asserted-by":"crossref","unstructured":"Kuang, K., Xiong, R., Cui, P., Athey, S., Li, B.: Stable prediction with model misspecification and agnostic distribution shift. In: Proceedings of the AAAI Conference on Artificial Intelligence (2020)","DOI":"10.1609\/aaai.v34i04.5876"},{"key":"23_CR11","doi-asserted-by":"crossref","unstructured":"Li, C., et al.: Multi-interest network with dynamic routing for recommendation at TMALL. In: Proceedings of the 28th ACM International Conference on Information and Knowledge Management (2019)","DOI":"10.1145\/3357384.3357814"},{"key":"23_CR12","doi-asserted-by":"crossref","unstructured":"Li, H., Wang, X., Zhang, Z., Zhu, W.: OOD-GNN: Out-of-distribution generalized graph neural network. arXiv preprint arXiv:2112.03806 (2021)","DOI":"10.1109\/TKDE.2022.3193725"},{"key":"23_CR13","doi-asserted-by":"crossref","unstructured":"Luo, Y., Liu, Z., Liu, Q.: Deep stable representation learning on electronic health records. arXiv preprint arXiv:2209.01321 (2022)","DOI":"10.1109\/ICDM54844.2022.00134"},{"key":"23_CR14","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 (2017)","DOI":"10.1145\/3097983.3098088"},{"key":"23_CR15","doi-asserted-by":"crossref","unstructured":"Ma, F., You, Q., Xiao, H., Chitta, R., Zhou, J., Gao, J.: Kame: knowledge-based attention model for diagnosis prediction in healthcare. In: Proceedings of the 27th ACM International Conference on Information and Knowledge Management, pp. 743\u2013752 (2018)","DOI":"10.1145\/3269206.3271701"},{"key":"23_CR16","unstructured":"Paszke, A., et al.: Pytorch: an imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems (2019)"},{"key":"23_CR17","doi-asserted-by":"crossref","unstructured":"Pennington, J., Socher, R., Manning, C.D.: Glove: global vectors for word representation. In: Empirical Methods in Natural Language Processing (EMNLP) (2014)","DOI":"10.3115\/v1\/D14-1162"},{"key":"23_CR18","doi-asserted-by":"crossref","unstructured":"Shen, Z., Cui, P., Kuang, K., Li, B., Chen, P.: Causally regularized learning with agnostic data selection bias. In: Proceedings of the 26th ACM International Conference on Multimedia (2018)","DOI":"10.1145\/3240508.3240577"},{"key":"23_CR19","unstructured":"Thulasiraman, K., Swamy, M.N.: Graphs: Theory and Algorithms. John Wiley & Sons, New York (2011)"},{"key":"23_CR20","unstructured":"Vaswani, A.,et al.: Attention is all you need. In: Proceedings of the 31st International Conference on Neural Information Processing Systems (2017)"},{"key":"23_CR21","doi-asserted-by":"crossref","unstructured":"Xu, Y., Biswal, S., Deshpande, S.R., Maher, K.O., Sun, J.: RAIM: recurrent attentive and intensive model of multimodal patient monitoring data. In: Proceedings of the 24th ACM SIGKDD international conference on Knowledge Discovery and Data Mining (2018)","DOI":"10.1145\/3219819.3220051"},{"key":"23_CR22","doi-asserted-by":"crossref","unstructured":"Yin, C., Zhao, R., Qian, B., Lv, X., Zhang, P.: Domain knowledge guided deep learning with electronic health records. In: 2019 IEEE International Conference on Data Mining (2019)","DOI":"10.1109\/ICDM.2019.00084"},{"key":"23_CR23","doi-asserted-by":"crossref","unstructured":"Zhang, M., King, C.R., Avidan, M., Chen, Y.: Hierarchical attention propagation for healthcare representation learning. In: Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining (2020)","DOI":"10.1145\/3394486.3403067"},{"key":"23_CR24","doi-asserted-by":"crossref","unstructured":"Zhang, X., Qian, B., Li, Y., Yin, C., Wang, X., Zheng, Q.: KnowRisk: an interpretable knowledge-guided model for disease risk prediction. In: 2019 IEEE International Conference on Data Mining (ICDM) (2019)","DOI":"10.1109\/ICDM.2019.00196"},{"key":"23_CR25","doi-asserted-by":"crossref","unstructured":"Zhang, X., Cui, P., Xu, R., Zhou, L., He, Y., Shen, Z.: Deep stable learning for out-of-distribution generalization. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (2021)","DOI":"10.1109\/CVPR46437.2021.00533"}],"container-title":["Communications in Computer and Information Science","Data Mining and Big Data"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-981-19-9297-1_23","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,3,21]],"date-time":"2023-03-21T15:50:39Z","timestamp":1679413839000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-981-19-9297-1_23"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022]]},"ISBN":["9789811992964","9789811992971"],"references-count":25,"URL":"https:\/\/doi.org\/10.1007\/978-981-19-9297-1_23","relation":{},"ISSN":["1865-0929","1865-0937"],"issn-type":[{"type":"print","value":"1865-0929"},{"type":"electronic","value":"1865-0937"}],"subject":[],"published":{"date-parts":[[2022]]},"assertion":[{"value":"20 January 2023","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"DMBD","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Data Mining and Big Data","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":"21 November 2022","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"24 November 2022","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"dmbd2022","order":10,"name":"conference_id","label":"Conference ID","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":"Easychair","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"135","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":"62","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":"46% - 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":"2.8","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":"2-3","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)"}}]}}