Editorial analysis: Rapid differentiation between self-limited Epstein-Barr virus infectious mononucleosis and life-threatening EBV-associated hemophagocytic lymphohistiocytosis (EBV-HLH) reshapes priorities for early triage models and feature selection in clinical ML workflows. Reported facts: Two independent retrospective studies published 27 June 2026 developed and validated ML classifiers for pediatric EBV-HLH. BMC Medical Informatics and Decision Making (Yingying Ye et al.) reports a XGBoost model trained on 1,026 hospitalized children that achieved AUC 0.9775, sensitivity 0.9461, and specificity 0.9784, with SHAP identifying D-dimer, cervical lymphadenopathy, GGT, LDH, and CD3+CD4+ T cells as top predictors. BMC Infectious Diseases (Li Xiao et al.) reports an external-validation cohort of 4,871 patients, EBV-HLH prevalence 12.46%, evaluation of 13 algorithms, and SHAP-based interpretation using routine CBC within 24 hours of admission.
Machine Learning and the Random Walk Puzzle: Forecasting the CAD/USD Exchange Rate with Expanding Window Evaluation and SHAP Interpretability