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Background <p>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).</p> Methods <p>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.</p> Results <p>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.</p> Conclusions <p>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.</p>

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

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年9月24日

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

摘要

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.