Objective:

To develop and validate an interpretable machine learning model for the preliminary risk stratification of viral encephalitis based on routine blood analysis.

Approach:

Key Findings:

• The XGBoost model achieved an AUC of 0.949 (95% CI: 0.921 ~ 0.978) in the training set and 0.900 (95% CI: 0.801–1.000) in the test set.
• Serum albumin and white blood cell counts, along with low neutrophil counts, were identified as significant predictors of viral encephalitis.
• Interactions between serum albumin and white blood cell counts were also influential in predictions.

Interpretation:

Limitations:

• The study is retrospective and conducted in a single tertiary hospital, which may limit generalizability.
• Patients without cerebrospinal fluid testing were excluded, which may affect the diagnostic accuracy.

Conclusion:

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