{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,1]],"date-time":"2026-10-01T22:48:10Z","timestamp":1790894890726,"version":"4.1.0"},"reference-count":24,"publisher":"Elsevier BV","issue":"5","license":[{"start":{"date-parts":[[2015,8,1]],"date-time":"2015-08-01T00:00:00Z","timestamp":1438387200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"}],"funder":[{"name":"Margaret Miller Research Fund"}],"content-domain":{"domain":["burnsjournal.com","elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["Burns"],"published-print":{"date-parts":[[2015,8]]},"DOI":"10.1016\/j.burns.2015.03.016","type":"journal-article","created":{"date-parts":[[2015,4,27]],"date-time":"2015-04-27T12:57:48Z","timestamp":1430139468000},"page":"925-934","update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":67,"title":["Mortality risk prediction in burn injury: Comparison of logistic regression with machine learning approaches"],"prefix":"10.1016","volume":"41","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-3445-724X","authenticated-orcid":false,"given":"Neophytos","family":"Stylianou","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Artur","family":"Akbarov","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Evangelos","family":"Kontopantelis","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Iain","family":"Buchan","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ken W.","family":"Dunn","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"78","reference":[{"key":"10.1016\/j.burns.2015.03.016_bib0125","series-title":"Standards and strategy for burn care","author":"Dunn","year":"2001"},{"key":"10.1016\/j.burns.2015.03.016_bib0130","doi-asserted-by":"crossref","first-page":"835","DOI":"10.1016\/j.burns.2012.12.010","article-title":"Predicting survival in thermal injury: a systematic review of methodology of composite prediction models","volume":"39","author":"Hussain","year":"2013","journal-title":"Burns"},{"key":"10.1016\/j.burns.2015.03.016_bib0135","doi-asserted-by":"crossref","first-page":"260","DOI":"10.1016\/S0196-0644(82)80096-6","article-title":"The abbreviated burn severity index","volume":"11","author":"Tobiasen","year":"1982","journal-title":"Ann Emerg Med"},{"key":"10.1016\/j.burns.2015.03.016_bib0140","doi-asserted-by":"crossref","first-page":"111","DOI":"10.1002\/bjs.6329","article-title":"Development and validation of a model for prediction of mortality in patients with acute burn injury","volume":"96","author":"The Belgian Outcome in Burn Injury Study Group","year":"2009","journal-title":"Br J Surg"},{"key":"10.1016\/j.burns.2015.03.016_bib0145","series-title":"Foundations of machine learning","author":"Mehryar","year":"2012"},{"key":"10.1016\/j.burns.2015.03.016_bib0150","series-title":"The elements of statistical learning: data mining inference, and prediction","author":"Hastie","year":"2011"},{"key":"10.1016\/j.burns.2015.03.016_bib0155","doi-asserted-by":"crossref","first-page":"1316","DOI":"10.1016\/j.burns.2014.04.013","article-title":"A model of British in-hospital mortality among burns patients","volume":"40","author":"Stylianou","year":"2014","journal-title":"Burns"},{"key":"10.1016\/j.burns.2015.03.016_bib0160","series-title":"R: A language and environment for statistical computing","author":"R Core Team","year":"2014"},{"key":"10.1016\/j.burns.2015.03.016_bib0165","unstructured":"High Throughput Computing Using Condor at Manchester. http:\/\/condor.eps.manchester.ac.uk\/ [accessed 20.09.14]."},{"key":"10.1016\/j.burns.2015.03.016_bib0170","doi-asserted-by":"crossref","first-page":"29","DOI":"10.1148\/radiology.143.1.7063747","article-title":"The meaning and use of the area under a receiver operating characteristic (ROC) curve","volume":"143","author":"Hanley","year":"1982","journal-title":"Radiology"},{"key":"10.1016\/j.burns.2015.03.016_bib0175","doi-asserted-by":"crossref","first-page":"457","DOI":"10.1002\/bimj.200810443","article-title":"The performance of risk prediction models","volume":"50","author":"Gerds","year":"2008","journal-title":"Biom J"},{"key":"10.1016\/j.burns.2015.03.016_bib0180","doi-asserted-by":"crossref","first-page":"239","DOI":"10.1023\/A:1024068626366","article-title":"Inference for the generalization error","volume":"52","author":"Nadeau","year":"2003","journal-title":"Mach Learn"},{"key":"10.1016\/j.burns.2015.03.016_bib0185","series-title":"Applied logistic regression","author":"Hosmer","year":"2013"},{"key":"10.1016\/j.burns.2015.03.016_bib0190","series-title":"Clinical prediction models: a practical approach to development, validation, and updating","author":"Steyerberg","year":"2008"},{"key":"10.1016\/j.burns.2015.03.016_bib0195","doi-asserted-by":"crossref","first-page":"734","DOI":"10.1093\/ejcts\/ezs043","article-title":"EuroSCORE II","volume":"41","author":"Nashef","year":"2012","journal-title":"Eur J Cardiothorac Surg"},{"key":"10.1016\/j.burns.2015.03.016_bib0200","series-title":"Electronic statistics textbook","author":"StatSoft","year":"2013"},{"key":"10.1016\/j.burns.2015.03.016_bib0205","doi-asserted-by":"crossref","first-page":"579","DOI":"10.1016\/S0305-4179(02)00045-1","article-title":"Role of artificial neural networks in prediction of survival of burn patients\u2014a new approach","volume":"28","author":"Estahbanati","year":"2002","journal-title":"Burns"},{"key":"10.1016\/j.burns.2015.03.016_bib0210","series-title":"Modern applied statistics with S","author":"Venables","year":"2002"},{"key":"10.1016\/j.burns.2015.03.016_bib0215","series-title":"Proc. Fifth Annu. Work. Comput. Learn. Theory \u2013 COLT\u201992","first-page":"144","article-title":"A training algorithm for optimal margin classifiers","author":"Boser","year":"1992"},{"key":"10.1016\/j.burns.2015.03.016_bib0220","doi-asserted-by":"crossref","first-page":"16","DOI":"10.1186\/1472-6947-10-16","article-title":"Application of support vector machine modeling for prediction of common diseases: the case of diabetes and pre-diabetes","volume":"10","author":"Yu","year":"2010","journal-title":"BMC Med Inform Decis Mak"},{"key":"10.1016\/j.burns.2015.03.016_bib0225","series-title":"e1071: Misc Functions of the Department of Statistics (e1071), TU Wien. R package version 1.6-1","author":"Meyer","year":"2012"},{"key":"10.1016\/j.burns.2015.03.016_bib0230","doi-asserted-by":"crossref","first-page":"5","DOI":"10.1023\/A:1010933404324","article-title":"Random forests","volume":"45","author":"Breiman","year":"2001","journal-title":"Mach Learn"},{"key":"10.1016\/j.burns.2015.03.016_bib0235","doi-asserted-by":"crossref","first-page":"e96385","DOI":"10.1371\/journal.pone.0096385","article-title":"A random forest based risk model for reliable and accurate prediction of receipt of transfusion in patients undergoing percutaneous coronary intervention","volume":"9","author":"Gurm","year":"2014","journal-title":"PLOS ONE"},{"key":"10.1016\/j.burns.2015.03.016_bib0240","first-page":"18","article-title":"Classification and regression by randomForest","volume":"2","author":"Liaw","year":"2002","journal-title":"R News"}],"container-title":["Burns"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0305417915000959?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0305417915000959?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2018,9,22]],"date-time":"2018-09-22T22:57:44Z","timestamp":1537657064000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S0305417915000959"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2015,8]]},"references-count":24,"journal-issue":{"issue":"5","published-print":{"date-parts":[[2015,8]]}},"alternative-id":["S0305417915000959"],"URL":"https:\/\/doi.org\/10.1016\/j.burns.2015.03.016","relation":{},"ISSN":["0305-4179"],"issn-type":[{"value":"0305-4179","type":"print"}],"subject":[],"published":{"date-parts":[[2015,8]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"Mortality risk prediction in burn injury: Comparison of logistic regression with machine learning approaches","name":"articletitle","label":"Article Title"},{"value":"Burns","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.burns.2015.03.016","name":"articlelink","label":"CrossRef DOI link to publisher maintained version"},{"value":"article","name":"content_type","label":"Content Type"},{"value":"Copyright \u00a9 2015 Elsevier Ltd and ISBI. All rights reserved.","name":"copyright","label":"Copyright"}]}}