# Machine learning identifies EBV-associated HLH from routine labs

> Source: <https://letsdatascience.com/news/machine-learning-identifies-ebv-associated-hlh-from-routine-637e5244>
> Published: 2026-06-30 10:00:00+00:00

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.