{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,26]],"date-time":"2025-03-26T16:13:20Z","timestamp":1743005600345,"version":"3.40.3"},"publisher-location":"Cham","reference-count":14,"publisher":"Springer Nature Switzerland","isbn-type":[{"type":"print","value":"9783031734991"},{"type":"electronic","value":"9783031735004"}],"license":[{"start":{"date-parts":[[2024,11,16]],"date-time":"2024-11-16T00:00:00Z","timestamp":1731715200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,11,16]],"date-time":"2024-11-16T00:00:00Z","timestamp":1731715200000},"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":[[2025]]},"DOI":"10.1007\/978-3-031-73500-4_3","type":"book-chapter","created":{"date-parts":[[2024,11,15]],"date-time":"2024-11-15T03:59:19Z","timestamp":1731643159000},"page":"26-37","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Modeling Temporal Dynamics in\u00a0Irregular ICU Data Using MWTA-LSTM"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0009-0002-1840-9325","authenticated-orcid":false,"given":"Mamadou Ben Hamidou","family":"Cissoko","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9354-6525","authenticated-orcid":false,"given":"Vincent","family":"Castelain","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4318-4252","authenticated-orcid":false,"given":"Nicolas","family":"Lachiche","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2024,11,16]]},"reference":[{"key":"3_CR1","doi-asserted-by":"crossref","unstructured":"Xiao, C., Choi, E., Sun, J.: Opportunities and challenges in developing deep learning models using electronic health records data: a systematic review. In: JAMIA, pp. 1419\u20131428 (2017)","DOI":"10.1093\/jamia\/ocy068"},{"key":"3_CR2","doi-asserted-by":"crossref","unstructured":"Pham, T., Tran, T., Phung, D., Venkatesh, S.: Predicting healthcare trajectories from medical records: a deep learning approach. J. Biomed. Inform., 218\u2013229. Elsevier (2017)","DOI":"10.1016\/j.jbi.2017.04.001"},{"key":"3_CR3","doi-asserted-by":"crossref","unstructured":"Baytas, I.M., et al.: Patient subtyping via time aware LSTM networks, In: 23rd ACM SIGKDD, pp. 65\u201374 (2017)","DOI":"10.1145\/3097983.3097997"},{"key":"3_CR4","doi-asserted-by":"crossref","unstructured":"Zheng, K., Gao, J., Ngiam, K.Y.: Resolving the bias in electronic medical records. In: Proceedings of the 23rd ACM SIGKDD, pp. 2171\u20132180 (2017)","DOI":"10.1145\/3097983.3098149"},{"key":"3_CR5","doi-asserted-by":"crossref","unstructured":"Zhang, Y.: ATTAIN: attention-based time-aware LSTM networks for disease progression modeling. IJCAI-2019, 4369\u20134375, Macao, China (2019)","DOI":"10.24963\/ijcai.2019\/607"},{"key":"3_CR6","unstructured":"Choi, E., Bahadori, M.T., Sun, J.: Retain: an interpretable predictive model for healthcare using reverse time attention mechanism: In: Advances in Neural Information Processing Systems (2016)"},{"key":"3_CR7","doi-asserted-by":"crossref","unstructured":"Zhu, Y., Li, H., Liao, Y.: What to Do Next: modeling user behaviors by Time-LSTM. IJCAI, 3602\u20133608 (2017)","DOI":"10.24963\/ijcai.2017\/504"},{"key":"3_CR8","doi-asserted-by":"crossref","unstructured":"Luo, J., Ye, M.: HiTANet: hierarchical time-aware attention networks for risk prediction on electronic health records, In: Proceedings of the 26th ACM SIGKDD, pp. 647\u2013656 (2020)","DOI":"10.1145\/3394486.3403107"},{"key":"3_CR9","unstructured":"Shukla, S.N., Marlin, B.: Multi-time attention networks for irregularly sampled time series. In: ICLR (2021)"},{"key":"3_CR10","doi-asserted-by":"crossref","unstructured":"Le Gall, J.-R., Lemeshow, S.: A new simplified acute physiology score (SAPS II) based on a European\/North American multicenter study. JAMA, 2957\u20132963 (1993)","DOI":"10.1001\/jama.270.24.2957"},{"key":"3_CR11","doi-asserted-by":"crossref","unstructured":"Hochreiter, S., Schmidhuber, J.: Long short-term memory. Neural Comput. J., 1735\u20131780. Neural Computation MIT Press (1997)","DOI":"10.1162\/neco.1997.9.8.1735"},{"key":"3_CR12","doi-asserted-by":"crossref","unstructured":"Johnson, A.E.W., Pollard, T.J.: MIMIC-III, a freely accessible critical care database. Sci. Data, 1\u20139. Neural computation Nature Publishing Group (2016)","DOI":"10.1038\/sdata.2016.35"},{"key":"3_CR13","doi-asserted-by":"crossref","unstructured":"Pollard, T.J., Johnson, A.E.W.: The eICU collaborative research database, a freely available multi-center database for critical care research. Sci. Data, pp. 1\u201313. Neural computation Nature Publishing Group(2018)","DOI":"10.1038\/sdata.2018.178"},{"key":"3_CR14","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, pp. 647\u2013656 (2020)","DOI":"10.1145\/3394486.3403107"}],"container-title":["Lecture Notes in Computer Science","Progress in Artificial Intelligence"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-73500-4_3","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,11,15]],"date-time":"2024-11-15T05:10:59Z","timestamp":1731647459000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-73500-4_3"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,11,16]]},"ISBN":["9783031734991","9783031735004"],"references-count":14,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-73500-4_3","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2024,11,16]]},"assertion":[{"value":"16 November 2024","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"EPIA","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"EPIA Conference on Artificial Intelligence","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Viana do Castelo","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Portugal","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2024","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"3 September 2024","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"6 September 2024","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"23","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"epia2024","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/epia2024.pt","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}