{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,4]],"date-time":"2026-09-04T02:39:14Z","timestamp":1788489554035,"version":"build-2803163510"},"reference-count":40,"publisher":"Wiley","issue":"3","license":[{"start":{"date-parts":[[2019,1,29]],"date-time":"2019-01-29T00:00:00Z","timestamp":1548720000000},"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":["Pediatric Transplantation"],"published-print":{"date-parts":[[2019,5]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Background<\/jats:title>\n                    <jats:p>Optimizing transplant candidates\u2019 priority for donor organs depends on the accurate assessment of post\u2010transplant outcomes. Due to the complexity of transplantation and the wide range of possible serious complications, recipient outcomes are difficult to predict accurately using conventional multivariable regression. Therefore, we evaluated the utility of 3 ML algorithms for predicting mortality after pediatric HTx.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>We identified patients &lt;18\u00a0years of age receiving HTx in 2006\u20102015 in the UNOS Registry database. Mortality within 1, 3, or 5\u00a0years was predicted using classification and regression trees, RFs, and ANN. Each model was trained using cross\u2010validation, then validated in a separate testing set. Model performance was primarily evaluated by the area under the receiver operating characteristic (AUC) curve.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>The training set included 2802 patients, whereas 700 were included in the testing set. RF achieved the best fit to the training data with AUCs of 0.74, 0.68, and 0.64 for 1\u2010, 3\u2010, and 5\u2010year mortality, respectively, and performed best in the testing data, with AUCs of 0.72, 0.61, and 0.60, respectively. Nevertheless, sensitivity was poor across models (training: 0.22\u20100.58; testing: 0.07\u20100.49).<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Discussion<\/jats:title>\n                    <jats:p>ML algorithms demonstrated fair predictive utility in both training and testing data, but the sensitivity of these algorithms was generally poor. With the registry missing data on many determinants of long\u2010term survival, the ability of ML methods to predict mortality after pediatric HTx may be fundamentally limited.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1111\/petr.13360","type":"journal-article","created":{"date-parts":[[2019,1,29]],"date-time":"2019-01-29T23:02:32Z","timestamp":1548802952000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":40,"title":["Prediction of mortality following pediatric heart transplant using machine learning algorithms"],"prefix":"10.1111","volume":"23","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-9629-4848","authenticated-orcid":false,"given":"Rebecca","family":"Miller","sequence":"first","affiliation":[{"name":"Department of Anesthesiology and Pain Medicine Nationwide Children\u2019s Hospital Columbus Ohio"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Dmitry","family":"Tumin","sequence":"additional","affiliation":[{"name":"Department of Pediatrics Brody School of Medicine, East Carolina University Greenville North Carolina"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jennifer","family":"Cooper","sequence":"additional","affiliation":[{"name":"The Research Institute Nationwide Children\u2019s Hospital Columbus Ohio"},{"name":"Department of Pediatrics The Ohio State University College of Medicine Columbus Ohio"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6734-6052","authenticated-orcid":false,"suffix":"Jr","given":"Don","family":"Hayes","sequence":"additional","affiliation":[{"name":"Section of Pulmonary Medicine Nationwide Children\u2019s Hospital Columbus Ohio"},{"name":"Department of Pulmonary and Critical Care Medicine The Ohio State University College of Medicine Columbus Ohio"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Joseph D.","family":"Tobias","sequence":"additional","affiliation":[{"name":"Department of Anesthesiology and Pain Medicine Nationwide Children\u2019s Hospital Columbus Ohio"},{"name":"Department of Anesthesiology and Pain Medicine The Ohio State University College of Medicine Columbus Ohio"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"311","published-online":{"date-parts":[[2019,1,29]]},"reference":[{"key":"e_1_2_8_2_1","unstructured":"About transplantation.Organ Procurement and Transplantation Network. 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