{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,10]],"date-time":"2026-05-10T18:30:07Z","timestamp":1778437807084,"version":"3.51.4"},"reference-count":34,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2021,1,18]],"date-time":"2021-01-18T00:00:00Z","timestamp":1610928000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Pediatr."],"abstract":"<jats:p><jats:bold>Background:<\/jats:bold> It is not only important for counseling purposes and for healthcare management. This study investigates the prediction accuracy of an artificial intelligence (AI)-based approach and a linear model. The heuristic expecting 1 day of stay per percentage of total body surface area (TBSA) serves as the performance benchmark.<\/jats:p><jats:p><jats:bold>Methods:<\/jats:bold> The study is based on pediatric burn patient's data sets from an international burn registry (<jats:italic>N<\/jats:italic> = 8,542). Mean absolute error and standard error are calculated for each prediction model (rule of thumb, linear regression, and random forest). Factors contributing to a prolonged stay and the relationship between TBSA and the residual error are analyzed.<\/jats:p><jats:p><jats:bold>Results:<\/jats:bold> The random forest-based approach and the linear model are statistically superior to the rule of thumb (<jats:italic>p<\/jats:italic> &amp;lt; 0.001, resp. <jats:italic>p<\/jats:italic> = 0.009). The residual error rises as TBSA increases for all methods. Factors associated with a prolonged LOS are particularly TBSA, depth of burn, and inhalation trauma.<\/jats:p><jats:p><jats:bold>Conclusion:<\/jats:bold> Applying AI-based algorithms to data from large international registries constitutes a promising tool for the purpose of prediction in medicine in the future; however, certain prerequisites concerning the underlying data sets and certain shortcomings must be considered.<\/jats:p>","DOI":"10.3389\/fped.2020.613736","type":"journal-article","created":{"date-parts":[[2021,1,18]],"date-time":"2021-01-18T14:36:53Z","timestamp":1610980613000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":9,"title":["Using Artificial Intelligence to Obtain More Evidence? 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