Journal Article

Machine learning model to predict mortality in patients with skin and soft tissue infection in emergency department

Wu, Kai-Hsiang, Hsiao, Cheng-Ting, Chang, Chia-Peng, Lin, Leng-Chieh, Chen, Yu-Wei, Wu, Po-Han, Hsieh, Chiao-Hsuan, Fann, Wen-Chih

2025

Overview

Background Accurately predicting mortality in patients with skin and soft-tissue infections (SSTIs) remains challenging. Machine learning models offer rapid processing, algorithmic impartiality, and strong predictive accuracy, which may improve early risk stratification in the emergency department (ED). Methods We retrospectively analyzed clinical data from 1,294 ED patients diagnosed with SSTIs between March 2015 and December 2020. Five machine learning algorithms—logistic regression (LR), k-nearest neighbours (KNN), support vector machine (SVM), random forest (RF), and Extreme Gradient Boosting (XGBoost)—were developed using 20 candidate variables, with model performance evaluated in independent runs. A simplified XGBoost model using only the six most influential predictors was also derived for bedside application. Results Among the five models, XGBoost achieved the highest performance (AUC = 0.892, sensitivity = 86.9%, specificity = 93.4%). The streamlined six-variable XGBoost model further improved predictive metrics (AUC = 0.922, sensitivity = 88.5%, specificity = 95.4%), matching or slightly surpassing the full model while reducing data requirements. Conclusions XGBoost outperformed LR, KNN, SVM, and RF in predicting SSTI mortality, offering both higher accuracy and operational efficiency. Its sequential tree-building, regularization, and robust handling of missing data enable superior discrimination in tabular clinical datasets. The simplified model, requiring only standard admission variables, provides a fast, cost-effective, and highly accurate tool for early identification of high-risk patients in the ED.

• Publisher: BioMed Central,BioMed Central Ltd,Springer Nature B.V,BMC
• Subject: Abscesses / Accuracy / Adult / Aged / Algorithms / Applications / Artificial intelligence / Artificial Intelligence in Pre-hospital and Critical Care: Innovations / Blood pressure / C-reactive protein / Cellulitis / Connective tissue diseases / Creatinine / Data mining / Deep learning / Diabetes / Diagnosis / Emergency medical care / Emergency Medicine / Emergency service / Emergency Service, Hospital / Female / Future Directions / Hospital Mortality - trends / Hospitals / Humans / Infections / Infectious skin diseases / Kidney diseases / Leukocytes / Liver cirrhosis / Machine Learning / Male / Medical prognosis / Medicine / Medicine & Public Health / Methods / Middle Aged / Mortality / Neural networks / Optimization / Patient outcomes / Patients / Prognosis / Retrospective Studies / Risk Assessment - methods / Skin / Skin and soft tissue infection / Skin Diseases, Infectious - mortality / Soft Tissue Infections - mortality / Traumatic Surgery / Variables