Novel application of an automated-machine learning development tool for predicting burn sepsis: proof of concept

Nam K. Tran, Samer Albahra, Tam N. Pham, James H. Holmes, David Greenhalgh, Tina L Palmieri, Jeffery Wajda, Hooman Rashidi

Research output: Contribution to journalArticle

Abstract

Sepsis is the primary cause of burn-related mortality and morbidity. 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 ≥ 18 years) with severe burn injury (≥ 20% total body surface area) to generate training and test datasets for ML applications. The MILO approach was compared against an exhaustive “non-automated” 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 1.99–4.04, P = 0.032). 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 k-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.

Original languageEnglish (US)
Article number12354
JournalScientific reports
Volume10
Issue number1
DOIs
StatePublished - Dec 1 2020

ASJC Scopus subject areas

  • General

Fingerprint Dive into the research topics of 'Novel application of an automated-machine learning development tool for predicting burn sepsis: proof of concept'. Together they form a unique fingerprint.

  • Cite this