AI Medical Compendium Journal:
Burns : journal of the International Society for Burn Injuries

Showing 21 to 24 of 24 articles

Machine learning in burn care and research: A systematic review of the literature.

Burns : journal of the International Society for Burn Injuries Jul 29, 2015
BACKGROUND: To date, there are no reviews on machine learning (ML) in burn care. Considering the growth of ML in medicine and the complexities and challenges of burn care, this review specializes on ML applications in burn care. The objective was to ...
Humans Anti-Infective Agents Survival Rate Biomedical Research Machine Learning Algorithms Aminoglycosides Length of Stay Wound Healing Burns Body Surface Area
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Influence of the central venous site on the transpulmonary thermodilution parameters in critically ill burn patients.

Burns : journal of the International Society for Burn Injuries Jul 20, 2015
The aim of this study was to verify the measurement concordance of cardiac index (CI), extra-vascular lung water index (EVLWI) and global end diastolic volume index (GEDVI) with transpulmonary thermodilution (TPTD) between the jugular and femoral acc...
Femoral Vein Radiography Thermodilution Aged Blood Flow Velocity Fluid Therapy Burns Blood Volume Jugular Veins Adult Lung Cardiac Output Critical Illness Extravascular Lung Water Male Humans Catheterization, Central Venous Female Middle Aged Prospective Studies
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Features identification for automatic burn classification.

Burns : journal of the International Society for Burn Injuries Jul 15, 2015
PURPOSE: In this paper an automatic system to diagnose burn depths based on colour digital photographs is presented.
Support Vector Machine Skin Sensitivity and Specificity Diagnosis, Computer-Assisted Photography Trauma Severity Indices Burn Units Humans Burns Databases, Factual Algorithms Automation Reproducibility of Results
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Mortality risk prediction in burn injury: Comparison of logistic regression with machine learning approaches.

Burns : journal of the International Society for Burn Injuries Apr 27, 2015
INTRODUCTION: Predicting mortality from burn injury has traditionally employed logistic regression models. Alternative machine learning methods have been introduced in some areas of clinical prediction as the necessary software and computational faci...
Smoke Inhalation Injury Body Surface Area Wales Models, Statistical Bayes Theorem Registries Burns Neural Networks, Computer Support Vector Machine Machine Learning Age Factors ROC Curve Decision Support Techniques Logistic Models Female Software Infant Humans Male England Risk Assessment Young Adult Adolescent Adult Child, Preschool Child
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