ArticleFrontiers in neurology
Explainable machine learning-based preliminary screening for viral encephalitis by blood routine analysis.
Article in Frontiers in neurology. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Authors and funding
4 authors.
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Abstract
Background: Viral encephalitis (VE) is a severe neurological emergency; however, timely diagnosis remains challenging, particularly in resource-limited settings. This study aimed to develop and validate an interpretable machine learning (ML) model for the preliminary risk stratification of VE based solely on routine blood analysis (RBA). Methods: A retrospective cohort of patients ( Results: The XGBoost model demonstrated superior performance with an AUC of 0.949 (95%CI: 0.921 ~ 0.978) in 10-fold cross-validation in the train set and 0.900 (95% CI: 0.801-1.000) in the test set. SHAP analysis identified serum albumin (ALB) and white blood cell (WBC) counts, and low neutrophil (NEU) counts were the most significant contributors to VE prediction. Notably, the interactions between ALB and WBC were also highly influential in VE prediction. Conclusion: This study presents an accurate and explainable XGBoost model for VE preliminary screening. Based on universally available blood indicators, it serves as a practical front-end tool for optimizing diagnostic workflows in emergency or primary care settings.
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