{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,13]],"date-time":"2026-07-13T06:13:30Z","timestamp":1783923210911,"version":"3.55.0"},"publisher-location":"Singapore","reference-count":24,"publisher":"Springer Nature Singapore","isbn-type":[{"value":"9789819234400","type":"print"},{"value":"9789819234417","type":"electronic"}],"license":[{"start":{"date-parts":[[2026,7,14]],"date-time":"2026-07-14T00:00:00Z","timestamp":1783987200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2026,7,14]],"date-time":"2026-07-14T00:00:00Z","timestamp":1783987200000},"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":[[2027]]},"DOI":"10.1007\/978-981-92-3441-7_6","type":"book-chapter","created":{"date-parts":[[2026,7,13]],"date-time":"2026-07-13T05:39:49Z","timestamp":1783921189000},"page":"59-71","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Cross-Attentive Transformer with Uncertainty-Guided Domain Adaptation for TAVR Mortality Prediction"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0009-0001-8700-8181","authenticated-orcid":false,"given":"Jingyang","family":"Sun","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6695-753X","authenticated-orcid":false,"given":"Chenxi","family":"Huang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Demin","family":"Xu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0004-3531-3450","authenticated-orcid":false,"given":"Peiwen","family":"Wang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0002-9737-4481","authenticated-orcid":false,"given":"Rongcheng","family":"Ouyang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1552-9033","authenticated-orcid":false,"given":"Qinglang","family":"Guo","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2026,7,14]]},"reference":[{"issue":"6","key":"6_CR1","doi-asserted-by":"publisher","first-page":"768","DOI":"10.1093\/ehjqcco\/qcad002","volume":"9","author":"J Kwiecinski","year":"2023","unstructured":"Kwiecinski, J., et al.: Machine learning for prediction of all-cause mortality after transcatheter aortic valve implantation. European Heart Journal-Quality of Care and Clinical Outcomes. 9(6), 768\u2013777 (2023)","journal-title":"European Heart Journal-Quality of Care and Clinical Outcomes"},{"key":"6_CR2","doi-asserted-by":"publisher","first-page":"33","DOI":"10.1016\/j.carrev.2020.08.010","volume":"24","author":"P Agasthi","year":"2021","unstructured":"Agasthi, P., et al.: Artificial intelligence trumps TAVI2-SCORE and CoreValve score in predicting 1-year mortality post-transcatheter aortic valve replacement. Cardiovasc. Revasc. Med. 24, 33\u201341 (2021)","journal-title":"Cardiovasc. Revasc. Med."},{"issue":"24","key":"6_CR3","doi-asserted-by":"publisher","first-page":"8620","DOI":"10.3390\/jcm14248620","volume":"14","author":"W He","year":"2025","unstructured":"He, W., Luo, J., Yang, X.: The application of multimodal data fusion algorithm MULTINet in postoperative risk assessment of TAVR. J. Clin. Med. 14(24), 8620 (2025)","journal-title":"J. Clin. Med."},{"issue":"2","key":"6_CR4","doi-asserted-by":"publisher","DOI":"10.1016\/j.jacadv.2025.102168","volume":"4","author":"D Tomii","year":"2025","unstructured":"Tomii, D., et al.: Multimodal machine learning-based technical failure prediction in patients undergoing transcatheter aortic valve replacement. JACC Adv. 4(2), 102168 (2025)","journal-title":"JACC Adv."},{"issue":"4","key":"6_CR5","first-page":"299","volume":"11","author":"C Caldararu","year":"2016","unstructured":"Caldararu, C., Balanescu, S.: Modern use of echocardiography in transcatheter aortic valve replacement: an up-date. Maedica. 11(4), 299 (2016)","journal-title":"Maedica"},{"issue":"Suppl_1","key":"6_CR6","first-page":"A18328","volume":"136","author":"D Haberman","year":"2017","unstructured":"Haberman, D., Shimoni, S., George, J.: Blood urea predicts all-cause mortality in patients with severe aortic stenosis. Circulation. 136(Suppl_1), A18328\u2013A18328 (2017)","journal-title":"Circulation"},{"issue":"1","key":"6_CR7","doi-asserted-by":"publisher","first-page":"e18","DOI":"10.2459\/JCM.0000000000001230","volume":"23","author":"H Lehtola","year":"2022","unstructured":"Lehtola, H., et al.: B-type natriuretic peptide ability to predict mortality after transcatheter aortic valve replacement. J. Cardiovasc. Med. 23(1), e18\u2013e20 (2022)","journal-title":"J. Cardiovasc. Med."},{"key":"6_CR8","doi-asserted-by":"publisher","first-page":"421","DOI":"10.1007\/s00392-020-01759-x","volume":"110","author":"H Seoudy","year":"2021","unstructured":"Seoudy, H., et al.: Elevated high-sensitivity troponin T levels at 1-year follow-up are associated with increased long-term mortality after TAVR. Clin. Res. Cardiol. 110, 421\u2013428 (2021)","journal-title":"Clin. Res. Cardiol."},{"issue":"10","key":"6_CR9","doi-asserted-by":"publisher","DOI":"10.1161\/JAHA.120.020739","volume":"10","author":"M Schindler","year":"2021","unstructured":"Schindler, M., et al.: Postprocedural troponin elevation and mortality after transcatheter aortic valve implantation. J. Am. Heart Assoc. 10(10), e020739 (2021)","journal-title":"J. Am. Heart Assoc."},{"issue":"3","key":"6_CR10","doi-asserted-by":"publisher","first-page":"240","DOI":"10.1016\/j.jjcc.2021.08.030","volume":"79","author":"T Imamura","year":"2022","unstructured":"Imamura, T., et al.: Clinical implications of troponin-T elevations following TAVR: troponin increase following TAVR. J. Cardiol. 79(3), 240\u2013246 (2022)","journal-title":"J. Cardiol."},{"key":"6_CR11","doi-asserted-by":"publisher","first-page":"71","DOI":"10.1016\/j.jelectrocard.2020.06.001","volume":"61","author":"K Gulsen","year":"2020","unstructured":"Gulsen, K., et al.: The effect of P wave indices on new onset atrial fibrillation after trans-catheter aortic valve replacement. J. Electrocardiol. 61, 71\u201376 (2020)","journal-title":"J. Electrocardiol."},{"issue":"5","key":"6_CR12","doi-asserted-by":"publisher","first-page":"2597","DOI":"10.1002\/ehf2.12837","volume":"7","author":"J Hoffmann","year":"2020","unstructured":"Hoffmann, J., et al.: Inflammatory signatures are associated with increased mortality after transfemoral transcatheter aortic valve implantation. ESC Heart Fail. 7(5), 2597\u20132610 (2020)","journal-title":"ESC Heart Fail."},{"key":"6_CR13","doi-asserted-by":"publisher","first-page":"S313","DOI":"10.1016\/j.ihj.2018.08.002","volume":"70","author":"C Khalil","year":"2018","unstructured":"Khalil, C., et al.: Neutrophil-to-lymphocyte ratio predicts heart failure readmissions and outcomes in patients undergoing transcatheter aortic valve replacement. Indian Heart J. 70, S313\u2013S318 (2018)","journal-title":"Indian Heart J."},{"key":"6_CR14","doi-asserted-by":"publisher","first-page":"168","DOI":"10.1007\/s12928-019-00592-y","volume":"35","author":"N Prasitlumkum","year":"2020","unstructured":"Prasitlumkum, N., et al.: Delirium is associated with higher mortality in transcatheter aortic valve replacement: systemic review and meta-analysis. Cardiovasc. Interv. Ther. 35, 168\u2013176 (2020)","journal-title":"Cardiovasc. Interv. Ther."},{"issue":"59","key":"6_CR15","first-page":"1","volume":"17","author":"Y Ganin","year":"2016","unstructured":"Ganin, Y., Lempitsky, V.: Domain-adversarial training of neural networks. J. Mach. Learn. Res. 17(59), 1\u201335 (2016)","journal-title":"J. Mach. Learn. Res."},{"issue":"11","key":"6_CR16","doi-asserted-by":"publisher","first-page":"e745","DOI":"10.1016\/S2589-7500(21)00208-9","volume":"3","author":"M Ghassemi","year":"2021","unstructured":"Ghassemi, M., Oakden-Rayner, L., Beam, A.L.: The false hope of current approaches to explainable artificial intelligence in health care. Lancet Digit. Health. 3(11), e745\u2013e750 (2021)","journal-title":"Lancet Digit. Health"},{"issue":"4","key":"6_CR17","doi-asserted-by":"publisher","DOI":"10.1016\/j.patter.2020.100049","volume":"1","author":"R Tomsett","year":"2020","unstructured":"Tomsett, R., et al.: Rapid trust calibration through interpretable and uncertainty-aware AI. Patterns. 1(4), 100049 (2020)","journal-title":"Patterns"},{"key":"6_CR18","doi-asserted-by":"publisher","DOI":"10.1016\/j.cosrev.2024.100720","volume":"56","author":"LP Le","year":"2025","unstructured":"Le, L.P., et al.: Multimodal missing data in healthcare: a comprehensive review and future directions. Comput Sci Rev. 56, 100720 (2025)","journal-title":"Comput Sci Rev"},{"key":"6_CR19","doi-asserted-by":"publisher","first-page":"1","DOI":"10.18637\/jss.v045.i03","volume":"45","author":"S Van Buuren","year":"2011","unstructured":"Van Buuren, S., Groothuis-Oudshoorn, K.: Mice: multivariate imputation by chained equations in R. J. Stat. Softw. 45, 1\u201367 (2011)","journal-title":"J. Stat. Softw."},{"key":"6_CR20","doi-asserted-by":"publisher","first-page":"1555585","DOI":"10.3389\/fonc.2025.1555585","volume":"15","author":"Q Xie","year":"2025","unstructured":"Xie, Q., et al.: SMoFFI-SegFormer: a novel approach for ovarian tumor segmentation based on an improved SegFormer architecture. Front. Oncol. 15, 1555585 (2025)","journal-title":"Front. Oncol."},{"issue":"3","key":"6_CR21","first-page":"4841","volume":"82","author":"J Huang","year":"2025","unstructured":"Huang, J., et al.: DMHFR: decoder with multi-head feature receptors for tract image segmentation. Comput. Mater. Contin. 82(3), 4841\u20134862 (2025)","journal-title":"Comput. Mater. Contin."},{"key":"6_CR22","doi-asserted-by":"publisher","DOI":"10.7717\/peerj-cs.3346","volume":"11","author":"J Huang","year":"2025","unstructured":"Huang, J., et al.: Hierarchically enhanced feature fusion and loss prevention for prostate segmentation on micro-ultrasound images. PeerJ Comput. Sci. 11, e3346 (2025)","journal-title":"PeerJ Comput. Sci."},{"key":"6_CR23","first-page":"3183","volume":"31","author":"M Sensoy","year":"2018","unstructured":"Sensoy, M., Kaplan, L., Kandemir, M.: Evidential deep learning to quantify classification uncertainty. Adv. Neural Inf. Proces. Syst. 31, 3183\u20133192 (2018)","journal-title":"Adv. Neural Inf. Proces. Syst."},{"key":"6_CR24","series-title":"Proceedings of Machine Learning Research","first-page":"33833","volume-title":"Proceedings of the 40th International Conference on Machine Learning","author":"L Tao","year":"2023","unstructured":"Tao, L., Dong, M., Xu, C.: Dual focal loss for calibration. In: Krause, A., Brunskill, E., Cho, K., Engelhardt, B., Sabato, S., Scarlett, J. (eds.) Proceedings of the 40th International Conference on Machine Learning Proceedings of Machine Learning Research, vol. 202, pp. 33833\u201333849. PMLR (2023)"}],"container-title":["Lecture Notes in Computer Science","Advanced Intelligent Computing Technology and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-981-92-3441-7_6","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,7,13]],"date-time":"2026-07-13T05:39:51Z","timestamp":1783921191000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-981-92-3441-7_6"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,7,14]]},"ISBN":["9789819234400","9789819234417"],"references-count":24,"URL":"https:\/\/doi.org\/10.1007\/978-981-92-3441-7_6","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,7,14]]},"assertion":[{"value":"14 July 2026","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ICIC","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Intelligent Computing","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Toronto","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Canada","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2026","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"22 July 2026","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"26 July 2026","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"22","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"icic2026a","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"http:\/\/www.ic-icc.cn\/2026\/index.htm","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}