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

ABSTRACT:

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 predictor

SUBMITTER: Chen YW 

PROVIDER: S-EPMC12462322 | biostudies-literature | 2025 Sep

REPOSITORIES: biostudies-literature