Development and external validation of machine learning-based models to predict patients with cellulitis developing sepsis during hospitalisation
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View abstract on PubMed
Summary
This summary is machine-generated.Machine learning models can predict sepsis development in hospitalized cellulitis patients. Artificial neural network and boosting models showed superior performance and robustness in external validation, aiding early detection.
Area Of Science
- Medical informatics
- Clinical prediction modeling
- Machine learning in healthcare
Background
- Cellulitis is a common cause of hospitalization, with high mortality rates associated with sepsis.
- Existing stratification models for predicting sepsis in cellulitis patients have shown unsatisfactory performance in external validation.
Purpose Of The Study
- To develop and compare various machine learning models for predicting sepsis development in hospitalized patients with cellulitis.
- To evaluate the performance and robustness of these models in external validation.
Main Methods
- Retrospective cohort study involving two independent international cohorts.
- Development phase: 6695 patients with cellulitis (MIMIC-IV database) using machine learning algorithms.
- External validation phase: 2506 patients with cellulitis (YiduCloud database).
Main Results
- In internal validation, XGBoost achieved the highest AUC (0.780).
- In external validation, the Artificial Neural Network (ANN) model demonstrated the highest AUC (0.830), outperforming logistic regression (LR).
- Boosting and ANN models exhibited greater robustness compared to LR when variables were removed.
Conclusions
- Boosting and neural network models offer improved performance and robustness for predicting sepsis in cellulitis patients.
- These models can serve as valuable tools for early detection of sepsis in hospitalized cellulitis patients.
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