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Traditional indicators of sepsis exhibit poor performance when used in this unique population due to their underlying hypermetabolic and inflammatory response following burn injury. To address this challenge, we developed the Machine Intelligence Learning Optimizer (MILO), an automated machine learning (ML) platform, to automatically produce ML models for predicting burn sepsis. We conducted a retrospective analysis of 211 adult patients (age\u2009\u2265\u200918\u00a0years) with severe burn injury (\u2265\u200920% total body surface area) to generate training and test datasets for ML applications. The MILO approach was compared against an exhaustive \u201cnon-automated\u201d ML approach as well as standard statistical methods. For this study, traditional multivariate logistic regression (LR) identified seven predictors of burn sepsis when controlled for age and burn size (OR 2.8, 95% CI 1.99\u20134.04, P\u2009=\u20090.032). The area under the ROC (ROC-AUC) when using these seven predictors was 0.88. Next, the non-automated ML approach produced an optimal model based on LR using 16 out of the 23 features from the study dataset. Model accuracy was 86% with ROC-AUC of 0.96. In contrast, MILO identified a <jats:italic>k<\/jats:italic>-nearest neighbor-based model using only five features to be the best performer with an accuracy of 90% and a ROC-AUC of 0.96. Machine learning augments burn sepsis prediction. MILO identified models more quickly, with less required features, and found to be analytically superior to traditional ML approaches. Future studies are needed to clinically validate the performance of MILO-derived ML models for sepsis prediction.<\/jats:p>","DOI":"10.1038\/s41598-020-69433-w","type":"journal-article","created":{"date-parts":[[2020,7,23]],"date-time":"2020-07-23T10:05:06Z","timestamp":1595498706000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":46,"title":["Novel application of an automated-machine learning development tool for predicting burn sepsis: proof of concept"],"prefix":"10.1038","volume":"10","author":[{"given":"Nam K.","family":"Tran","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Samer","family":"Albahra","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Tam N.","family":"Pham","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"suffix":"IV","given":"James H.","family":"Holmes","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"David","family":"Greenhalgh","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Tina L.","family":"Palmieri","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jeffery","family":"Wajda","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hooman H.","family":"Rashidi","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2020,7,23]]},"reference":[{"issue":"6","key":"69433_CR1","doi-asserted-by":"publisher","first-page":"776","DOI":"10.1097\/BCR.0b013e3181599bc9","volume":"28","author":"DG Greenhalgh","year":"2007","unstructured":"Greenhalgh, D. 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