BMC medical informatics and decision making · 2026
Development and validation of machine learning models for early diagnosis of hemophagocytic lymphohistiocytosis in pediatric Epstein-Barr virus infection.
СтатьяВалидационное исследованиеНазвание и аннотация: Перевод в очереди
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Ye Yingying, Li Yuqin, Su Yuewen, Cao Meng, Liu Jiaxue, Ding Jiaying, Hu Shaoyan, Tian Jianmei, Zhou Weifang
Аннотация
OBJECTIVE: To establish a machine learning (ML) model for the early diagnosis of Epstein-Barr virus-associated hemophagocytic lymphohistiocytosis (EBV-HLH) based on clinical features and to utilize the Shapley Additive Explanations (SHAP) method to interpret the ML model, thereby providing reliable factors for diagnosing EBV-HLH.
METHODS: We collected clinical data from 1,026 children with Epstein-Barr virus (EBV) infection who were hospitalized at Children's Hospital of Soochow University from October 2017 to September 2024. First, we compared the clinical data of Epstein-Barr virus-associated infectious mononucleosis (EBV-IM) and EBV-HLH through univariate analysis and least absolute shrinkage and selection operator (LASSO) regression to select key features for machine learning training. Subsequently, we applied six machine learning algorithms and logistic regression to build diagnostic models, and the optimal model was selected based on multiple evaluation metrics. Finally, we utilized the Shapley Additive Explanations (SHAP) algorithm to clarify the importance of variables in the model to facilitate its application in clinical settings.
RESULTS: Among the six machine learning models and logistic regression evaluated, the Extreme Gradient Boosting (XGBoost) model demonstrated the strongest discrimination ability, with an area under the receiver operating characteristic curve (AUC) of 0.9775, sensitivity of 0.9461 and specificity of 0.9784. The SHAP analysis indicated that the most important predictors for EBV-HLH were D-dimer, cervical lymphadenopathy (CLA), gamma-glutamyl transferase (GGT), lactate dehydrogenase (LDH), and CD3 + CD4+ T cells.
CONCLUSIONS: The XGBoost model demonstrated excellent predictive performance for the early identification of EBV-HLH in children. Compared with other models, it achieved higher sensitivity and may serve as a promising decision-support tool pending external validation.
CLINICAL TRIAL NUMBER: Not applicable.
METHODS: We collected clinical data from 1,026 children with Epstein-Barr virus (EBV) infection who were hospitalized at Children's Hospital of Soochow University from October 2017 to September 2024. First, we compared the clinical data of Epstein-Barr virus-associated infectious mononucleosis (EBV-IM) and EBV-HLH through univariate analysis and least absolute shrinkage and selection operator (LASSO) regression to select key features for machine learning training. Subsequently, we applied six machine learning algorithms and logistic regression to build diagnostic models, and the optimal model was selected based on multiple evaluation metrics. Finally, we utilized the Shapley Additive Explanations (SHAP) algorithm to clarify the importance of variables in the model to facilitate its application in clinical settings.
RESULTS: Among the six machine learning models and logistic regression evaluated, the Extreme Gradient Boosting (XGBoost) model demonstrated the strongest discrimination ability, with an area under the receiver operating characteristic curve (AUC) of 0.9775, sensitivity of 0.9461 and specificity of 0.9784. The SHAP analysis indicated that the most important predictors for EBV-HLH were D-dimer, cervical lymphadenopathy (CLA), gamma-glutamyl transferase (GGT), lactate dehydrogenase (LDH), and CD3 + CD4+ T cells.
CONCLUSIONS: The XGBoost model demonstrated excellent predictive performance for the early identification of EBV-HLH in children. Compared with other models, it achieved higher sensitivity and may serve as a promising decision-support tool pending external validation.
CLINICAL TRIAL NUMBER: Not applicable.