{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,17]],"date-time":"2026-08-17T15:01:31Z","timestamp":1786978891560,"version":"build-2736575974"},"publisher-location":"Cham","reference-count":30,"publisher":"Springer Nature Switzerland","isbn-type":[{"value":"9783031703775","type":"print"},{"value":"9783031703782","type":"electronic"}],"license":[{"start":{"date-parts":[[2024,1,1]],"date-time":"2024-01-01T00:00:00Z","timestamp":1704067200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,1,1]],"date-time":"2024-01-01T00:00:00Z","timestamp":1704067200000},"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":[[2024]]},"DOI":"10.1007\/978-3-031-70378-2_21","type":"book-chapter","created":{"date-parts":[[2024,9,1]],"date-time":"2024-09-01T05:02:05Z","timestamp":1725166925000},"page":"335-350","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["Boosting Patient Representation Learning via\u00a0Graph Contrastive Learning"],"prefix":"10.1007","author":[{"given":"Zhenhao","family":"Zhang","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yuxi","family":"Liu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jiang","family":"Bian","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Antonio Jimeno","family":"Yepes","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jun","family":"Shen","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Fuyi","family":"Li","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Guodong","family":"Long","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Flora D.","family":"Salim","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2024,8,22]]},"reference":[{"key":"21_CR1","doi-asserted-by":"crossref","unstructured":"Cai, D., Sun, C., Song, M., Zhang, B., Hong, S., Li, H.: Hypergraph contrastive learning for electronic health records. In: Proceedings of the 2022 SIAM International Conference on Data Mining (SDM), pp. 127\u2013135. SIAM (2022)","DOI":"10.1137\/1.9781611977172.15"},{"issue":"1","key":"21_CR2","doi-asserted-by":"publisher","first-page":"6085","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), 6085 (2018)","journal-title":"Sci. Rep."},{"key":"21_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":"21_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":"21_CR5","doi-asserted-by":"crossref","unstructured":"Choi, E., et al.: Learning the graphical structure of electronic health records with graph convolutional transformer. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol.\u00a034, pp. 606\u2013613 (2020)","DOI":"10.1609\/aaai.v34i01.5400"},{"issue":"3","key":"21_CR6","doi-asserted-by":"publisher","first-page":"42","DOI":"10.1109\/MSP.2021.3134634","volume":"39","author":"L Ericsson","year":"2022","unstructured":"Ericsson, L., Gouk, H., Loy, C.C., Hospedales, T.M.: Self-supervised representation learning: Introduction, advances, and challenges. IEEE Signal Process. Mag. 39(3), 42\u201362 (2022)","journal-title":"IEEE Signal Process. Mag."},{"issue":"1","key":"21_CR7","doi-asserted-by":"publisher","first-page":"96","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), 96 (2019)","journal-title":"Sci. Data"},{"issue":"1","key":"21_CR8","doi-asserted-by":"publisher","first-page":"51","DOI":"10.1038\/s41746-020-0249-z","volume":"3","author":"CB Hilton","year":"2020","unstructured":"Hilton, C.B., et al.: Personalized predictions of patient outcomes during and after hospitalization using artificial intelligence. NPJ Digital Med. 3(1), 51 (2020)","journal-title":"NPJ Digital Med."},{"key":"21_CR9","doi-asserted-by":"crossref","unstructured":"Huang, G., Ma, F.: Concad: contrastive learning-based cross attention for sleep apnea detection. In: Machine Learning and Knowledge Discovery in Databases. Applied Data Science Track: European Conference, ECML PKDD 2021, Bilbao, Spain, September 13\u201317, 2021, Proceedings, Part V 21, pp. 68\u201384. Springer (2021)","DOI":"10.1007\/978-3-030-86517-7_5"},{"issue":"3","key":"21_CR10","doi-asserted-by":"publisher","first-page":"225","DOI":"10.1016\/j.jvlc.2005.10.003","volume":"17","author":"X Huang","year":"2006","unstructured":"Huang, X., Lai, W.: Clustering graphs for visualization via node similarities. J. Visual Lang. Comput. 17(3), 225\u2013253 (2006)","journal-title":"J. Visual Lang. Comput."},{"key":"21_CR11","unstructured":"Kipf, T.N., Welling, M.: Semi-supervised classification with graph convolutional networks. arXiv preprint arXiv:1609.02907 (2016)"},{"key":"21_CR12","doi-asserted-by":"crossref","unstructured":"Liu, Y., Qin, S., Yepes, A.J., Shao, W., Zhang, Z., Salim, F.D.: Integrated convolutional and recurrent neural networks for health risk prediction using patient journey data with many missing values. In: 2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), pp. 1658\u20131663. IEEE (2022)","DOI":"10.1109\/BIBM55620.2022.9995048"},{"key":"21_CR13","doi-asserted-by":"crossref","unstructured":"Liu, Y., Qin, S., Zhang, Z., Shao, W.: Compound density networks for risk prediction using electronic health records. In: 2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), pp. 1078\u20131085. IEEE (2022)","DOI":"10.1109\/BIBM55620.2022.9995587"},{"key":"21_CR14","doi-asserted-by":"crossref","unstructured":"Liu, Y., Zhang, Z., Yepes, A.J., Salim, F.D.: Modeling long-term dependencies and short-term correlations in patient journey data with temporal attention networks for health prediction. In: Proceedings of the 13th ACM International Conference on Bioinformatics, Computational Biology and Health Informatics, pp. 1\u201310 (2022)","DOI":"10.1145\/3535508.3545535"},{"key":"21_CR15","doi-asserted-by":"crossref","unstructured":"Liu, Z., Li, X., Peng, H., He, L., Philip, S.Y.: Heterogeneous similarity graph neural network on electronic health records. In: 2020 IEEE International Conference on Big Data (Big Data). pp. 1196\u20131205. IEEE (2020)","DOI":"10.1109\/BigData50022.2020.9377795"},{"key":"21_CR16","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 International Conference on Knowledge Discovery & Data Mining, pp. 647\u2013656 (2020)","DOI":"10.1145\/3394486.3403107"},{"key":"21_CR17","doi-asserted-by":"publisher","DOI":"10.1016\/j.artmed.2022.102359","volume":"131","author":"JGD Ochoa","year":"2022","unstructured":"Ochoa, J.G.D., Mustafa, F.E.: Graph neural network modelling as a potentially effective method for predicting and analyzing procedures based on patients\u2019 diagnoses. Artif. Intell. Med. 131, 102359 (2022)","journal-title":"Artif. Intell. Med."},{"issue":"7","key":"21_CR18","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":"21_CR19","unstructured":"Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, \u0141., Polosukhin, I.: Attention is all you need. Advances in neural information processing systems 30 (2017)"},{"key":"21_CR20","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":"21_CR21","doi-asserted-by":"crossref","unstructured":"Wang, T., Jin, D., Wang, R., He, D., Huang, Y.: Powerful graph convolutional networks with adaptive propagation mechanism for homophily and heterophily. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol.\u00a036, pp. 4210\u20134218 (2022)","DOI":"10.1609\/aaai.v36i4.20340"},{"key":"21_CR22","unstructured":"Xie, J., Girshick, R., Farhadi, A.: Unsupervised deep embedding for clustering analysis. In: International Conference on Machine Learning, pp. 478\u2013487. PMLR (2016)"},{"key":"21_CR23","first-page":"5812","volume":"33","author":"Y You","year":"2020","unstructured":"You, Y., Chen, T., Sui, Y., Chen, T., Wang, Z., Shen, Y.: Graph contrastive learning with augmentations. Adv. Neural. Inf. Process. Syst. 33, 5812\u20135823 (2020)","journal-title":"Adv. Neural. Inf. Process. Syst."},{"key":"21_CR24","doi-asserted-by":"crossref","unstructured":"Yu, J., Xia, X., Chen, T., Cui, L., Hung, N.Q.V., Yin, H.: Xsimgcl: towards extremely simple graph contrastive learning for recommendation. IEEE Trans. Knowl. Data Eng. (2023)","DOI":"10.1109\/TKDE.2023.3288135"},{"key":"21_CR25","doi-asserted-by":"crossref","unstructured":"Zhang, Y.: Attain: attention-based time-aware lstm networks for disease progression modeling. In: In Proceedings of the 28th International Joint Conference on Artificial Intelligence (IJCAI-2019), pp. 4369-4375, Macao, China. (2019)","DOI":"10.24963\/ijcai.2019\/607"},{"key":"21_CR26","doi-asserted-by":"crossref","unstructured":"Zheng, Z., Tan, Y., Wang, H., Yu, S., Liu, T., Liang, C.: Casangcl: pre-training and fine-tuning model based on cascaded attention network and graph contrastive learning for molecular property prediction. Briefings Bioinform. 24(1), bbac566 (2023)","DOI":"10.1093\/bib\/bbac566"},{"key":"21_CR27","doi-asserted-by":"crossref","unstructured":"Zhu, W., Razavian, N.: Variationally regularized graph-based representation learning for electronic health records. In: Proceedings of the Conference on Health, Inference, and Learning, pp. 1\u201313 (2021)","DOI":"10.1145\/3450439.3451855"},{"key":"21_CR28","unstructured":"Zhu, Y., Xu, Y., Liu, Q., Wu, S.: An empirical study of graph contrastive learning. arXiv preprint arXiv:2109.01116 (2021)"},{"key":"21_CR29","unstructured":"Zhu, Y., Xu, Y., Yu, F., Liu, Q., Wu, S., Wang, L.: Deep graph contrastive representation learning. arXiv preprint arXiv:2006.04131 (2020)"},{"key":"21_CR30","doi-asserted-by":"crossref","unstructured":"Zhu, Y., Xu, Y., Yu, F., Liu, Q., Wu, S., Wang, L.: Graph contrastive learning with adaptive augmentation. In: Proceedings of the Web Conference 2021, pp. 2069\u20132080 (2021)","DOI":"10.1145\/3442381.3449802"}],"container-title":["Lecture Notes in Computer Science","Machine Learning and Knowledge Discovery in Databases. Applied Data Science Track"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-70378-2_21","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,9,1]],"date-time":"2024-09-01T05:06:21Z","timestamp":1725167181000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-70378-2_21"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024]]},"ISBN":["9783031703775","9783031703782"],"references-count":30,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-70378-2_21","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024]]},"assertion":[{"value":"22 August 2024","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"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":"Vilnius","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Lithuania","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":"8 September 2024","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"12 September 2024","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"24","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"ecml2024","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/2024.ecmlpkdd.org\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}