Construction and validation of a predictive model for meningoencephalitis in pediatric scrub typhus based on machine learning algorithms.

Abstract

To retrospectively analyze the clinical characteristics of pediatric scrub typhus (ST) with meningoencephalitis (STME) and to construct and validate predictive models using machine learning.Clinical data were collected from 100 cases of pediatric STME and matched with data from 100 ST cases without meningitis using propensity-score matching. Risk factors for STME in pediatrics were identified through the least absolute shrinkage and selection operator (LASSO) regression analysis. Six predictive models-Logistic Regression, K-Nearest Neighbors, Naive Bayes, Multi-layer Perceptron(MLP), Random Forest, and XGBoost-were constructed using the training set and evaluated for performance, with validation conducted on the test set. The Shapley Additive Explanations (SHAP) method was applied to rank the importance of each variable.All children improved and were discharged following treatment with azithromycin/doxycycline (1/99). Twelve variable features were identified through the LASSO regression. Of the six predictive models developed, the XGBoost model demonstrated the highest performance in the training set (AUC = 0.926), though its performance in the test set was moderate (AUC = 0.740). The MLP model exhibited robust predictive performance in both training and test sets, with AUCs of 0.897 and 0.817, respectively. Clinical decision curve analysis indicated that the MLP and XGBoost models provide significant clinical utility. SHAP analysis identified the most important predictors for STME as ferritin, white blood cell count, edema, prothrombin time, fibrinogen, duration of pre-admission fever, eschar, activated partial thromboplastin time, splenomegaly, and headache. The MLP and XGBoost models showed strong predictive capability for pediatric STME, with favorable outcomes following doxycycline-based therapy.