Explainable machine learning for predicting infections that require hospitalization in patients with systemic lupus erythematosus

Key Points

• The aim is to create an interpretable machine learning model to predict serious infections in patients with systemic lupus erythematosus (SLE).
• Developed an interpretable machine learning model for predicting infections
• Utilized SHAP-based explanations for clinical transparency
• Conducted a retrospective analysis at a single center
• The model effectively identifies high-risk individuals for serious infections
• SHAP explanations enhance understanding of model predictions
• Further validation is necessary due to the study's limitations