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

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 multiv

SUBMITTER: Tran NK 

PROVIDER: S-EPMC7378181 | biostudies-literature | 2020 Jul

REPOSITORIES: biostudies-literature