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Novel application of an automated-machine learning development tool for predicting burn sepsis: proof of concept
- Source :
- Scientific reports, vol 10, iss 1, Scientific Reports, Scientific Reports, Vol 10, Iss 1, Pp 1-9 (2020)
- Publication Year :
- 2020
- Publisher :
- eScholarship, University of California, 2020.
-
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.
- Subjects :
- Male
Burn injury
Future studies
Databases, Factual
lcsh:Medicine
computer.software_genre
Logistic regression
Machine Learning
0302 clinical medicine
Models
Medicine
lcsh:Science
education.field_of_study
screening and diagnosis
Multidisciplinary
Age Factors
Scientific data
Hematology
Middle Aged
Survival Rate
Detection
Infectious Diseases
Proof of concept
030220 oncology & carcinogenesis
Predictive value of tests
Infectious diseases
Female
Burns
Adult
Physical Injury - Accidents and Adverse Effects
Population
Machine learning
Trauma
Models, Biological
Article
Disease-Free Survival
Sepsis
03 medical and health sciences
Databases
Predictive Value of Tests
Humans
Severe burn
education
Factual
business.industry
Inflammatory and immune system
lcsh:R
030208 emergency & critical care medicine
medicine.disease
Biological
4.1 Discovery and preclinical testing of markers and technologies
Good Health and Well Being
lcsh:Q
Artificial intelligence
business
computer
Software
Subjects
Details
- Database :
- OpenAIRE
- Journal :
- Scientific reports, vol 10, iss 1, Scientific Reports, Scientific Reports, Vol 10, Iss 1, Pp 1-9 (2020)
- Accession number :
- edsair.doi.dedup.....06a512d0bd13bf053049c968ee45335c