{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,4]],"date-time":"2026-09-04T02:38:47Z","timestamp":1788489527147,"version":"build-2803163510"},"reference-count":27,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2022,11,14]],"date-time":"2022-11-14T00:00:00Z","timestamp":1668384000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2022,11,14]],"date-time":"2022-11-14T00:00:00Z","timestamp":1668384000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"funder":[{"DOI":"10.13039\/501100004359","name":"Vetenskapsr\u00e5det","doi-asserted-by":"publisher","award":["2019-00487"],"award-info":[{"award-number":["2019-00487"]}],"id":[{"id":"10.13039\/501100004359","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001858","name":"VINNOVA","doi-asserted-by":"publisher","award":["2017-04689"],"award-info":[{"award-number":["2017-04689"]}],"id":[{"id":"10.13039\/501100001858","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100003793","name":"Hj\u00e4rt-Lungfonden","doi-asserted-by":"publisher","award":["20190623"],"award-info":[{"award-number":["20190623"]}],"id":[{"id":"10.13039\/501100003793","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Anna-Lisa and Sven Eric Lundgrens Foundation"},{"name":"Region Skane research funds"},{"name":"Skane University Hospital"},{"name":"Government grant for clinical research"},{"name":"Familjen Hjelms Stiftelse f\u00f6r medicinsk forskning"},{"DOI":"10.13039\/501100003252","name":"Lund University","doi-asserted-by":"crossref","id":[{"id":"10.13039\/501100003252","id-type":"DOI","asserted-by":"crossref"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Sci Rep"],"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:p>\n                    The most limiting factor in heart transplantation is the lack of donor organs. With enhanced prediction of outcome, it may be possible to increase the life-years from the organs that become available. Applications of machine learning to tabular data, typical of clinical decision support, pose the practical question of interpretation, which has technical and potential ethical implications. In particular, there is an issue of principle about the predictability of complex data and whether this is inherent in the data or strongly dependent on the choice of machine learning model, leading to the so-called accuracy-interpretability trade-off. We model 1-year mortality in heart transplantation data with a self-explaining neural network, which is benchmarked against a deep learning model on the same development data, in an external validation study with two data sets: (1) UNOS transplants in 2017\u20132018 (n\u2009=\u20094750) for which the self-explaining and deep learning models are comparable in their AUROC 0.628 [0.602,0.654]\n                    <jats:italic>cf.<\/jats:italic>\n                    0.635 [0.609,0.662] and (2) Scandinavian transplants during 1997\u20132018 (n\u2009=\u20092293), showing good calibration with AUROCs of 0.626 [0.588,0.665] and 0.634 [0.570, 0.698], respectively, with and without missing data (n\u2009=\u2009982). This shows that for tabular data, predictive models can be transparent and capture important nonlinearities, retaining full predictive performance.\n                  <\/jats:p>","DOI":"10.1038\/s41598-022-23817-2","type":"journal-article","created":{"date-parts":[[2022,11,14]],"date-time":"2022-11-14T16:03:04Z","timestamp":1668441784000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":22,"title":["Enhanced survival prediction using explainable artificial intelligence in heart transplantation"],"prefix":"10.1038","volume":"12","author":[{"given":"Paulo J. 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An early investigation of outcomes with the new 2018 donor heart allocation system in the United States. J. Heart Lung Transplant. 39, 1\u20134. https:\/\/doi.org\/10.1016\/j.healun.2019.11.002 (2020).","journal-title":"J. Heart Lung Transplant."},{"key":"23817_CR2","doi-asserted-by":"publisher","first-page":"557","DOI":"10.1016\/j.jchf.2020.03.014","volume":"8","author":"EM Hsich","year":"2020","unstructured":"Hsich, E. M. et al. Heart transplantation: An in-depth survival analysis. JACC Heart Fail. 8, 557\u2013568. https:\/\/doi.org\/10.1016\/j.jchf.2020.03.014 (2020).","journal-title":"JACC Heart Fail."},{"key":"23817_CR3","doi-asserted-by":"publisher","first-page":"89","DOI":"10.1038\/s41746-022-00637-2","volume":"5","author":"N Gotlieb","year":"2022","unstructured":"Gotlieb, N. et al. The promise of machine learning applications in solid organ transplantation. NPJ Digit. Med. 5, 89. https:\/\/doi.org\/10.1038\/s41746-022-00637-2 (2022).","journal-title":"NPJ Digit. 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