Diagnostic Machine Learning Models of Infectious Mononucleosis in Children Based on Clinical Data: A Retrospective Multicenter Study
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Le résumé fourni par la source
The clinical manifestations of infectious mononucleosis (IM) and acute respiratory tract infections (ARTI) exhibit significant similarities. We aim to develop cost-efficient models for IM in children utilizing the Shapley Additive explanation (SHAP) algorithm. We conducted a retrospective analysis of patients with the first diagnosis of IM from three medical centers. We employed four different machine learning techniques to develop new diagnostic models based on clinical features and serum inflammatory markers. The predictive accuracy of model was evaluated using the ROC curve and compared with traditional indicators. This study included a total of 853 patients with 49 clinical features. Through ten-fold cross-validation, the best-performing integrated learning models are GBM, XGBoost, and RSF. The models were interpreted using SHAP to derive the feature subsets Lymphocyte, PLR, LDH, SII, Age, these subsets comprised the final diagnostic prediction model. The results show that the models based on five indicators have the same IM diagnostic performance as the EBV-specific examination, and have a higher diagnostic value than the diagnosis based on atypical lymphocytes and EBV-DNA load. Meanwhile, our models are applicable to children with IM of different age groups. This study provides a new diagnostic tool for differentiating IM from ARTI in children. Our novel diagnostic models are independent of EBV-specific test results and exhibit superior diagnostic performance compared to traditional markers in the diagnosis of IM, particularly for primary healthcare units and institutions lacking EBV-specific detection capabilities.
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Le contrôle bibliographique ouvert
DOI retrouvé dans Crossref DOI retrouvé ; titre concordant.
- Titre Crossref
- Diagnostic Machine Learning Models of Infectious Mononucleosis in Children Based on Clinical Data: A Retrospective Multicenter Study
- Date Crossref
- 31/07/2025
- Éditeur
- Wiley
- Type
- journal-article
Ce recoupement confirme des métadonnées liées au DOI. Il ne confirme ni la méthode ni les conclusions de l’étude, et il ne compte pas comme une seconde source scientifique indépendante.
Où se fait cette recherche
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Sun Yat-sen University State Key Laboratory of Oncology in South China pays non établi dans la noticeUniversité ou école supérieure
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Guangdong Medical College pays non établi dans la noticeUniversité ou école supérieure
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Sun Yat-sen Memorial Hospital pays non établi dans la noticeÉtablissement de santé
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Sun Yat-sen University Cancer Center pays non établi dans la noticeÉtablissement de santé
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Guangzhou Women and Children Medical Center pays non établi dans la noticeÉtablissement de santé
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Guangzhou Medical University Central Laboratory pays non établi dans la noticeUniversité ou école supérieure
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Qianjiang Central Hospital pays non établi dans la noticeÉtablissement de santé
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College of Medical Technology Guangdong Medical University Dongguan Guangdong P.R. China pays non établi dans la noticeUniversité ou école supérieure
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Guangdong Provincial Key Laboratory of Malignant Tumor Epigenetics and Gene Regulation pays non établi dans la noticeStructure de recherche
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Department of Clinical Laboratory Medicine Sun Yat‐Sen University Cancer Center State Key Laboratory of Oncology in South China pays non établi dans la noticeUniversité ou école supérieure
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Department of Clinical Laboratory Chongqing University Qianjiang Hospital Chongqing P.R. China Qianjiang Key Laboratory of Chongqing Qianjiang Central Hospital Laboratory Medicine pays non établi dans la noticeUniversité ou école supérieure
State Key Laboratory of Oncology in South China — Sun Yat-sen University, Guangdong Medical College et Sun Yat-sen Memorial Hospital, avec 8 autres affiliations.
Une affiliation ne permet pas de déduire la nationalité d’un auteur.