{"slug": "machine-learning-identifies-ebv-associated-hlh-from-routine-labs", "title": "Machine learning identifies EBV-associated HLH from routine labs", "summary": "Two independent studies published June 27, 2026, developed machine learning classifiers to distinguish life-threatening EBV-associated hemophagocytic lymphohistiocytosis (EBV-HLH) from self-limited Epstein-Barr virus infectious mononucleosis in children. The XGBoost model achieved an AUC of 0.9775, sensitivity of 0.9461, and specificity of 0.9784, with top predictors including D-dimer, cervical lymphadenopathy, GGT, LDH, and CD3+CD4+ T cells. The findings could improve early triage and feature selection in clinical ML workflows.", "body_md": "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.", "url": "https://wpnews.pro/news/machine-learning-identifies-ebv-associated-hlh-from-routine-labs", "canonical_source": "https://letsdatascience.com/news/machine-learning-identifies-ebv-associated-hlh-from-routine-637e5244", "published_at": "2026-06-30 10:00:00+00:00", "updated_at": "2026-06-30 10:26:06.387868+00:00", "lang": "en", "topics": ["machine-learning", "artificial-intelligence", "ai-research"], "entities": ["BMC Medical Informatics and Decision Making", "BMC Infectious Diseases", "XGBoost", "SHAP", "Yingying Ye", "Li Xiao"], "alternates": {"html": "https://wpnews.pro/news/machine-learning-identifies-ebv-associated-hlh-from-routine-labs", "markdown": "https://wpnews.pro/news/machine-learning-identifies-ebv-associated-hlh-from-routine-labs.md", "text": "https://wpnews.pro/news/machine-learning-identifies-ebv-associated-hlh-from-routine-labs.txt", "jsonld": "https://wpnews.pro/news/machine-learning-identifies-ebv-associated-hlh-from-routine-labs.jsonld"}}