Estimates of Total Burned Surface Area by Emergency Department Clinicians and Burn Specialists

Hahn, Barry; Roh, Seungwhan Alex; Price, Christopher; Fu, Wayne; Dibello, Jaclyn; Berwald, Nicole; Greenstein, Josh; Chacko, Jerel

doi:10.7759/cureus.9362

Cited by 2 publications

(1 citation statement)

References 19 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The severity of a burn injury can also be assessed using the “Rule of Nines”, which divides the body into different regions, each accounting for a specific percentage of the total body surface area (TBSA). This helps in estimating the extent of the burn and determining the need for specialized care [ 6 ].…”

Section: Introductionmentioning

confidence: 99%

Implementing AI Models for Prognostic Predictions in High-Risk Burn Patients

Yeh,

Lin,

Chen

et al. 2023

Diagnostics

View full text Add to dashboard Cite

Background and Objectives: Burn injuries range from minor medical issues to severe, life-threatening conditions. The severity and location of the burn dictate its treatment; while minor burns might be treatable at home, severe burns necessitate medical intervention, sometimes in specialized burn centers with extended follow-up care. This study aims to leverage artificial intelligence (AI)/machine learning (ML) to forecast potential adverse effects in burn patients. Methods: This retrospective analysis considered burn patients admitted to Chi Mei Medical Center from 2010 to 2019. The study employed 14 features, comprising supplementary information like prior comorbidities and laboratory results, for building models for predicting graft surgery, a prolonged hospital stay, and overall adverse effects. Overall, 70% of the data set trained the AI models, with the remaining 30% reserved for testing. Three ML algorithms of random forest, LightGBM, and logistic regression were employed with evaluation metrics of accuracy, sensitivity, specificity, and the area under the receiver operating characteristic curve (AUC). Results: In this research, out of 224 patients assessed, the random forest model yielded the highest AUC for predictions related to prolonged hospital stays (>14 days) at 81.1%, followed by the XGBoost (79.9%) and LightGBM (79.5%) models. Besides, the random forest model of the need for a skin graft showed the highest AUC (78.8%), while the random forest model and XGBoost model of the occurrence of adverse complications both demonstrated the highest AUC (87.2%) as well. Based on the best models with the highest AUC values, an AI prediction system is designed and integrated into hospital information systems to assist physicians in the decision-making process. Conclusions: AI techniques showcased exceptional capabilities for predicting a prolonged hospital stay, the need for a skin graft, and the occurrence of overall adverse complications for burn patients. The insights from our study fuel optimism for the inception of a novel predictive model that can seamlessly meld with hospital information systems, enhancing clinical decisions and bolstering physician–patient dialogues.

show abstract

Section: Introductionmentioning

confidence: 99%