{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,12]],"date-time":"2026-02-12T17:06:53Z","timestamp":1770916013603,"version":"3.50.1"},"publisher-location":"Cham","reference-count":40,"publisher":"Springer Nature Switzerland","isbn-type":[{"value":"9783031434266","type":"print"},{"value":"9783031434273","type":"electronic"}],"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\/978-3-031-43427-3_26","type":"book-chapter","created":{"date-parts":[[2023,9,16]],"date-time":"2023-09-16T21:01:41Z","timestamp":1694898101000},"page":"428-443","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":10,"title":["Contrastive Learning-Based Imputation-Prediction Networks for\u00a0In-hospital Mortality Risk Modeling Using EHRs"],"prefix":"10.1007","author":[{"given":"Yuxi","family":"Liu","sequence":"first","affiliation":[]},{"given":"Zhenhao","family":"Zhang","sequence":"additional","affiliation":[]},{"given":"Shaowen","family":"Qin","sequence":"additional","affiliation":[]},{"given":"Flora D.","family":"Salim","sequence":"additional","affiliation":[]},{"given":"Antonio Jimeno","family":"Yepes","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2023,9,17]]},"reference":[{"key":"26_CR1","unstructured":"Cao, W., Wang, D., Li, J., Zhou, H., Li, L., Li, Y.: Brits: bidirectional recurrent imputation for time series. In: Advances in Neural Information Processing Systems, vol. 31 (2018)"},{"issue":"1","key":"26_CR2","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1038\/s41598-018-24271-9","volume":"8","author":"Z Che","year":"2018","unstructured":"Che, Z., Purushotham, S., Cho, K., Sontag, D., Liu, Y.: Recurrent neural networks for multivariate time series with missing values. Sci. Rep. 8(1), 1\u201312 (2018)","journal-title":"Sci. Rep."},{"key":"26_CR3","unstructured":"Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597\u20131607. PMLR (2020)"},{"key":"26_CR4","doi-asserted-by":"crossref","unstructured":"Cho, K., et al.: Learning phrase representations using RNN encoder-decoder for statistical machine translation. arXiv preprint arXiv:1406.1078 (2014)","DOI":"10.3115\/v1\/D14-1179"},{"key":"26_CR5","doi-asserted-by":"crossref","unstructured":"Cui, S., Wang, J., Gui, X., Wang, T., Ma, F.: Automed: automated medical risk predictive modeling on electronic health records. In: 2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), pp. 948\u2013953. IEEE (2022)","DOI":"10.1109\/BIBM55620.2022.9995209"},{"issue":"11","key":"26_CR6","doi-asserted-by":"publisher","first-page":"139","DOI":"10.1145\/3422622","volume":"63","author":"I Goodfellow","year":"2020","unstructured":"Goodfellow, I., et al.: Generative adversarial networks. Commun. ACM 63(11), 139\u2013144 (2020)","journal-title":"Commun. ACM"},{"issue":"1","key":"26_CR7","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1186\/s41512-020-00077-0","volume":"4","author":"RH Groenwold","year":"2020","unstructured":"Groenwold, R.H.: Informative missingness in electronic health record systems: the curse of knowing. Diagn. Prognostic Res. 4(1), 1\u20136 (2020)","journal-title":"Diagn. Prognostic Res."},{"issue":"1","key":"26_CR8","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1038\/s41597-019-0103-9","volume":"6","author":"H Harutyunyan","year":"2019","unstructured":"Harutyunyan, H., Khachatrian, H., Kale, D.C., Ver Steeg, G., Galstyan, A.: Multitask learning and benchmarking with clinical time series data. Sci. Data 6(1), 1\u201318 (2019)","journal-title":"Sci. Data"},{"issue":"1","key":"26_CR9","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1038\/sdata.2016.35","volume":"3","author":"AE Johnson","year":"2016","unstructured":"Johnson, A.E., et al.: MIMIC-III, a freely accessible critical care database. Sci. Data 3(1), 1\u20139 (2016)","journal-title":"Sci. Data"},{"key":"26_CR10","first-page":"18661","volume":"33","author":"P Khosla","year":"2020","unstructured":"Khosla, P., et al.: Supervised contrastive learning. Adv. Neural. Inf. Process. Syst. 33, 18661\u201318673 (2020)","journal-title":"Adv. Neural. Inf. Process. Syst."},{"key":"26_CR11","unstructured":"Kingma, D.P., Ba, J.: Adam: a method for stochastic optimization. arXiv preprint arXiv:1412.6980 (2014)"},{"key":"26_CR12","doi-asserted-by":"publisher","first-page":"193907","DOI":"10.1109\/ACCESS.2020.3031549","volume":"8","author":"PH Le-Khac","year":"2020","unstructured":"Le-Khac, P.H., Healy, G., Smeaton, A.F.: Contrastive representation learning: a framework and review. IEEE Access 8, 193907\u2013193934 (2020)","journal-title":"IEEE Access"},{"issue":"8","key":"26_CR13","doi-asserted-by":"publisher","first-page":"4270","DOI":"10.1109\/JBHI.2022.3172549","volume":"26","author":"Y Lee","year":"2022","unstructured":"Lee, Y., Jun, E., Choi, J., Suk, H.I.: Multi-view integrative attention-based deep representation learning for irregular clinical time-series data. IEEE J. Biomed. Health Inform. 26(8), 4270\u20134280 (2022)","journal-title":"IEEE J. Biomed. Health Inform."},{"key":"26_CR14","doi-asserted-by":"crossref","unstructured":"Li, J., Shang, J., McAuley, J.: Uctopic: unsupervised contrastive learning for phrase representations and topic mining. arXiv preprint arXiv:2202.13469 (2022)","DOI":"10.18653\/v1\/2022.acl-long.426"},{"key":"26_CR15","doi-asserted-by":"publisher","first-page":"3606","DOI":"10.1109\/TIP.2022.3173163","volume":"31","author":"M Li","year":"2022","unstructured":"Li, M., Li, C.G., Guo, J.: Cluster-guided asymmetric contrastive learning for unsupervised person re-identification. IEEE Trans. Image Process. 31, 3606\u20133617 (2022)","journal-title":"IEEE Trans. Image Process."},{"key":"26_CR16","unstructured":"Li, R., Ma, F., Gao, J.: Integrating multimodal electronic health records for diagnosis prediction. In: AMIA Annual Symposium Proceedings, vol. 2021, p. 726. American Medical Informatics Association (2021)"},{"key":"26_CR17","unstructured":"Luo, Y., Cai, X., Zhang, Y., Xu, J., et al.: Multivariate time series imputation with generative adversarial networks. In: Advances in Neural Information Processing Systems, vol. 31 (2018)"},{"key":"26_CR18","doi-asserted-by":"crossref","unstructured":"Luo, Y., Zhang, Y., Cai, X., Yuan, X.: E2GAN: end-to-end generative adversarial network for multivariate time series imputation. In: Proceedings of the 28th International Joint Conference on Artificial Intelligence, pp. 3094\u20133100. AAAI Press (2019)","DOI":"10.24963\/ijcai.2019\/429"},{"key":"26_CR19","doi-asserted-by":"crossref","unstructured":"Ma, L., et al.: Adacare: explainable clinical health status representation learning via scale-adaptive feature extraction and recalibration. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 34, pp. 825\u2013832 (2020)","DOI":"10.1609\/aaai.v34i01.5427"},{"key":"26_CR20","doi-asserted-by":"crossref","unstructured":"Ma, L., et al.: Distilling knowledge from publicly available online EMR data to emerging epidemic for prognosis. In: Proceedings of the Web Conference 2021, pp. 3558\u20133568 (2021)","DOI":"10.1145\/3442381.3449855"},{"key":"26_CR21","doi-asserted-by":"crossref","unstructured":"Ma, L., et al.: Concare: personalized clinical feature embedding via capturing the healthcare context. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 34, pp. 833\u2013840 (2020)","DOI":"10.1609\/aaai.v34i01.5428"},{"issue":"2","key":"26_CR22","doi-asserted-by":"publisher","first-page":"818","DOI":"10.1109\/JBHI.2021.3098511","volume":"26","author":"N McCombe","year":"2021","unstructured":"McCombe, N., et al.: Practical strategies for extreme missing data imputation in dementia diagnosis. IEEE J. Biomed. Health Inform. 26(2), 818\u2013827 (2021)","journal-title":"IEEE J. Biomed. Health Inform."},{"issue":"9","key":"26_CR23","doi-asserted-by":"publisher","first-page":"9684","DOI":"10.1109\/TCYB.2021.3053599","volume":"52","author":"AW Mulyadi","year":"2021","unstructured":"Mulyadi, A.W., Jun, E., Suk, H.I.: Uncertainty-aware variational-recurrent imputation network for clinical time series. IEEE Trans. Cybern. 52(9), 9684\u20139694 (2021)","journal-title":"IEEE Trans. Cybern."},{"key":"26_CR24","doi-asserted-by":"publisher","DOI":"10.1016\/j.engappai.2022.105232","volume":"115","author":"Q Ni","year":"2022","unstructured":"Ni, Q., Cao, X.: MBGAN: an improved generative adversarial network with multi-head self-attention and bidirectional RNN for time series imputation. Eng. Appl. Artif. Intell. 115, 105232 (2022)","journal-title":"Eng. Appl. Artif. Intell."},{"key":"26_CR25","doi-asserted-by":"crossref","unstructured":"Oh, E., Kim, T., Ji, Y., Khyalia, S.: Sting: self-attention based time-series imputation networks using GAN. In: 2021 IEEE International Conference on Data Mining (ICDM), pp. 1264\u20131269. IEEE (2021)","DOI":"10.1109\/ICDM51629.2021.00155"},{"key":"26_CR26","doi-asserted-by":"crossref","unstructured":"Pang, B., et al.: Unsupervised representation for semantic segmentation by implicit cycle-attention contrastive learning. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 36, pp. 2044\u20132052 (2022)","DOI":"10.1609\/aaai.v36i2.20100"},{"issue":"8","key":"26_CR27","doi-asserted-by":"publisher","first-page":"4218","DOI":"10.1109\/JBHI.2022.3172656","volume":"26","author":"RC Pereira","year":"2022","unstructured":"Pereira, R.C., Abreu, P.H., Rodrigues, P.P.: Partial multiple imputation with variational autoencoders: tackling not at randomness in healthcare data. IEEE J. Biomed. Health Inform. 26(8), 4218\u20134227 (2022)","journal-title":"IEEE J. Biomed. Health Inform."},{"issue":"1","key":"26_CR28","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1038\/sdata.2018.178","volume":"5","author":"TJ Pollard","year":"2018","unstructured":"Pollard, T.J., Johnson, A.E., Raffa, J.D., Celi, L.A., Mark, R.G., Badawi, O.: The eICU collaborative research database, a freely available multi-center database for critical care research. Sci. Data 5(1), 1\u201313 (2018)","journal-title":"Sci. Data"},{"issue":"7","key":"26_CR29","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pone.0235424","volume":"15","author":"S Sheikhalishahi","year":"2020","unstructured":"Sheikhalishahi, S., Balaraman, V., Osmani, V.: Benchmarking machine learning models on multi-centre eicu critical care dataset. PLoS ONE 15(7), e0235424 (2020)","journal-title":"PLoS ONE"},{"key":"26_CR30","doi-asserted-by":"publisher","first-page":"607","DOI":"10.1016\/j.ins.2021.08.016","volume":"579","author":"Z Shi","year":"2021","unstructured":"Shi, Z., et al.: Deep dynamic imputation of clinical time series for mortality prediction. Inf. Sci. 579, 607\u2013622 (2021)","journal-title":"Inf. Sci."},{"key":"26_CR31","doi-asserted-by":"crossref","unstructured":"Tan, Q., et al.: Data-GRU: dual-attention time-aware gated recurrent unit for irregular multivariate time series. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 34, pp. 930\u2013937 (2020)","DOI":"10.1609\/aaai.v34i01.5440"},{"key":"26_CR32","unstructured":"Vaswani, A., et al.: Attention is all you need. In: Advances in Neural Information Processing Systems, pp. 5998\u20136008 (2017)"},{"key":"26_CR33","doi-asserted-by":"crossref","unstructured":"Wang, W., Zhou, T., Yu, F., Dai, J., Konukoglu, E., Van Gool, L.: Exploring cross-image pixel contrast for semantic segmentation. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision, pp. 7303\u20137313 (2021)","DOI":"10.1109\/ICCV48922.2021.00721"},{"key":"26_CR34","doi-asserted-by":"crossref","unstructured":"Wang, Y., Min, Y., Chen, X., Wu, J.: Multi-view graph contrastive representation learning for drug-drug interaction prediction. In: Proceedings of the Web Conference 2021, pp. 2921\u20132933 (2021)","DOI":"10.1145\/3442381.3449786"},{"issue":"6","key":"26_CR35","doi-asserted-by":"publisher","first-page":"2260","DOI":"10.1109\/JBHI.2020.3033323","volume":"25","author":"D Xu","year":"2020","unstructured":"Xu, D., Sheng, J.Q., Hu, P.J.H., Huang, T.S., Hsu, C.C.: A deep learning-based unsupervised method to impute missing values in patient records for improved management of cardiovascular patients. IEEE J. Biomed. Health Inform. 25(6), 2260\u20132272 (2020)","journal-title":"IEEE J. Biomed. Health Inform."},{"key":"26_CR36","doi-asserted-by":"crossref","unstructured":"Yang, C., An, Z., Cai, L., Xu, Y.: Mutual contrastive learning for visual representation learning. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 36, pp. 3045\u20133053 (2022)","DOI":"10.1609\/aaai.v36i3.20211"},{"key":"26_CR37","doi-asserted-by":"publisher","first-page":"2517","DOI":"10.1109\/LSP.2022.3224880","volume":"29","author":"AY Y\u0131ld\u0131z","year":"2022","unstructured":"Y\u0131ld\u0131z, A.Y., Ko\u00e7, E., Ko\u00e7, A.: Multivariate time series imputation with transformers. IEEE Signal Process. Lett. 29, 2517\u20132521 (2022)","journal-title":"IEEE Signal Process. Lett."},{"key":"26_CR38","doi-asserted-by":"crossref","unstructured":"Yuan, X., et al.: Multimodal contrastive training for visual representation learning. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 6995\u20137004 (2021)","DOI":"10.1109\/CVPR46437.2021.00692"},{"key":"26_CR39","doi-asserted-by":"crossref","unstructured":"Zang, C., Wang, F.: SCEHR: supervised contrastive learning for clinical risk prediction using electronic health records. In: Proceedings of IEEE International Conference on Data Mining, vol. 2021, pp. 857\u2013866 (2021)","DOI":"10.1109\/ICDM51629.2021.00097"},{"key":"26_CR40","doi-asserted-by":"publisher","first-page":"67","DOI":"10.1016\/j.ins.2020.11.035","volume":"551","author":"Y Zhang","year":"2021","unstructured":"Zhang, Y., Zhou, B., Cai, X., Guo, W., Ding, X., Yuan, X.: Missing value imputation in multivariate time series with end-to-end generative adversarial networks. Inf. Sci. 551, 67\u201382 (2021)","journal-title":"Inf. Sci."}],"container-title":["Lecture Notes in Computer Science","Machine Learning and Knowledge Discovery in Databases: Applied Data Science and Demo Track"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-43427-3_26","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,9,16]],"date-time":"2023-09-16T21:05:27Z","timestamp":1694898327000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-43427-3_26"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023]]},"ISBN":["9783031434266","9783031434273"],"references-count":40,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-43427-3_26","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023]]},"assertion":[{"value":"17 September 2023","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"The experimental datasets used for this work are obtained from the publicly available Medical Information Mart for Intensive Care (MIMIC-III) dataset and the eICU Collaborative Research dataset. These data were used under license. The authors declare that they have no conflicts of interest. This article does not contain any studies involving human participants performed by any of the authors.","order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethical Statement"}},{"value":"ECML PKDD","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Joint European Conference on Machine Learning and Knowledge Discovery in Databases","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Turin","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Italy","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":"18 September 2023","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"22 September 2023","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":"ecml2023","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/2023.ecmlpkdd.org\/","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":"CMT","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"829","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":"196","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":"24% - 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":"3.63","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":"4.5","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)"}},{"value":"Applied Data Science Track: 239 submissions, 58 accepted papers; Demo Track: 31 submissions, 16 accepted papers.","order":10,"name":"additional_info_on_review_process","label":"Additional Info on Review Process","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}}]}}