{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,2]],"date-time":"2026-10-02T09:47:22Z","timestamp":1790934442985,"version":"4.1.0"},"reference-count":28,"publisher":"Wiley","issue":"8","license":[{"start":{"date-parts":[[2021,6,29]],"date-time":"2021-06-29T00:00:00Z","timestamp":1624924800000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/onlinelibrary.wiley.com\/termsAndConditions#vor"}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Clinical Transplantation"],"published-print":{"date-parts":[[2021,8]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:sec><jats:title>Purpose<\/jats:title><jats:p>We sought to develop and validate machine learning (ML) models to increase the predictive accuracy of mortality after heart transplantation (HT).<\/jats:p><\/jats:sec><jats:sec><jats:title>Methods and results<\/jats:title><jats:p>We included adult HT recipients from the United Network for Organ Sharing (UNOS) database between 2010 and 2018 using solely pre\u2010transplant variables. The study cohort comprised 18\u00a0625 patients (53 \u00b1 13\u00a0years, 73% males) and was randomly split into a derivation and a validation cohort with a 3:1 ratio. At 1\u2010year after HT, there were 2334 (12.5%) deaths. Out of a total of 134 pre\u2010transplant variables, 39 were selected as highly predictive of 1\u2010year mortality via feature selection algorithm and were used to train five ML models. AUC for the prediction of 1\u2010year survival was .689, .642, .649, .637, .526 for the Adaboost, Logistic Regression, Decision Tree, Support Vector Machine, and K\u2010nearest neighbor models, respectively, whereas the Index for Mortality Prediction after Cardiac Transplantation (IMPACT) score had an AUC of .569. Local interpretable model\u2010agnostic explanations (LIME) analysis was used in the best performing model to identify the relative impact of key predictors. ML models for 3\u2010 and 5\u2010year survival as well as acute rejection were also developed in a secondary analysis and yielded AUCs of .629, .609, and .610 using 27, 31, and 91 selected variables respectively.<\/jats:p><\/jats:sec><jats:sec><jats:title>Conclusion<\/jats:title><jats:p>Machine learning models showed good predictive accuracy of outcomes after heart transplantation.<\/jats:p><\/jats:sec>","DOI":"10.1111\/ctr.14388","type":"journal-article","created":{"date-parts":[[2021,6,22]],"date-time":"2021-06-22T08:19:54Z","timestamp":1624349994000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":50,"title":["State\u2010of\u2010the\u2010art machine learning algorithms for the prediction of outcomes after contemporary heart transplantation: Results from the UNOS database"],"prefix":"10.1111","volume":"35","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-8352-5360","authenticated-orcid":false,"given":"Polydoros N.","family":"Kampaktsis","sequence":"first","affiliation":[{"name":"Division of Cardiology New York University Langone Medical Center  New York New York USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Aspasia","family":"Tzani","sequence":"additional","affiliation":[{"name":"Brigham and Women's Hospital Heart and Vascular Center Harvard Medical School  Boston Massachusetts USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ilias P.","family":"Doulamis","sequence":"additional","affiliation":[{"name":"Department of Cardiac Surgery Boston Children's Hospital, Harvard Medical School  Boston Massachusetts USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1090-2177","authenticated-orcid":false,"given":"Serafeim","family":"Moustakidis","sequence":"additional","affiliation":[{"name":"AIDEAS O\u00dc  Tallinn Estonia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Anastasios","family":"Drosou","sequence":"additional","affiliation":[{"name":"Centre for Research &amp; Technology Hellas Information Technologies Institute (CERTH\u2010ITI)  Thessaloniki Greece"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Nikolaos","family":"Diakos","sequence":"additional","affiliation":[{"name":"Division of Cardiology Columbia University Irving Medical Center  New York New York USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Stavros G.","family":"Drakos","sequence":"additional","affiliation":[{"name":"Division of Cardiovascular Medicine &amp; Nora Eccles Harrison Cardiovascular Research and Training Institute University of Utah Health &amp; School of Medicine  Salt Lake, Utah USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5740-9670","authenticated-orcid":false,"given":"Alexandros","family":"Briasoulis","sequence":"additional","affiliation":[{"name":"National and Kapodistrian University of Athens  Athens Greece"},{"name":"Division of Cardiovascular Medicine University of Iowa Carver College of Medicine  Iowa City Iowa USA"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"311","published-online":{"date-parts":[[2021,6,29]]},"reference":[{"key":"e_1_2_7_2_1","doi-asserted-by":"publisher","DOI":"10.1161\/CIR.0000000000000659"},{"key":"e_1_2_7_3_1","doi-asserted-by":"publisher","DOI":"10.1056\/NEJMoa1900486"},{"key":"e_1_2_7_4_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.jacc.2017.04.025"},{"key":"e_1_2_7_5_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.jacc.2013.05.019"},{"key":"e_1_2_7_6_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.jchf.2017.08.021"},{"key":"e_1_2_7_7_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.athoracsur.2011.04.030"},{"key":"e_1_2_7_8_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.healun.2011.10.004"},{"key":"e_1_2_7_9_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.athoracsur.2011.02.086"},{"key":"e_1_2_7_10_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.jcmg.2020.07.015"},{"key":"e_1_2_7_11_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.jacc.2020.08.017"},{"key":"e_1_2_7_12_1","doi-asserted-by":"publisher","DOI":"10.1161\/JAHA.117.008081"},{"key":"e_1_2_7_13_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.jchf.2020.01.012"},{"key":"e_1_2_7_14_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.healun.2020.01.658"},{"key":"e_1_2_7_15_1","doi-asserted-by":"publisher","DOI":"10.1038\/s41598-018-21417-7"},{"key":"e_1_2_7_16_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.patcog.2010.05.007"},{"key":"e_1_2_7_17_1","doi-asserted-by":"publisher","DOI":"10.1080\/10255842.2011.554408"},{"key":"e_1_2_7_18_1","doi-asserted-by":"publisher","DOI":"10.1006\/jcss.1997.1504"},{"key":"e_1_2_7_19_1","doi-asserted-by":"publisher","DOI":"10.1007\/BF00994018"},{"key":"e_1_2_7_20_1","volume-title":"Data Mining: Practical Machine Learning Tools and Techniques","author":"Witten IH","year":"2011"},{"key":"e_1_2_7_21_1","doi-asserted-by":"publisher","DOI":"10.1023\/A:1006559212014"},{"key":"e_1_2_7_22_1","doi-asserted-by":"crossref","unstructured":"RibeiroMT SinghS GuestrinC Why should I trust you? 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