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TRV-2026-0975Certified recordPeer-reviewed

Development of a Machine Learning Algorithm for Differential Diagnosis Between Primary Immune Thrombocytopenia and Connective Tissue Disease-Related Thrombocytopenia in Pediatric Patients

Background To develop a machine learning model for early differentiation of primary immune thrombocytopenia (pITP) from connective tissue disease-related thrombocytopenia (CTD-TP) in children presenting with thrombocytopenia. Method A retrospective study was conducted on 387 newly diagnosed children with thrombocytopenia. All patients were clinically diagnosed and divided into a training set and a test set in a 7:3 ratio. Six machine learning algorithms, including XGboost, RF, SVM, LR, GBDT, BPNN, were used to e…

Health · G Space — documented gain · certified 2026-09-04 · v1 · article view · machine-readable

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Development of a Machine Learning Algorithm for Differential Diagnosis Between Primary Immune Thrombocytopenia and Connective Tissue Disease-Related Thrombocytopenia in Pediatric Patients: Based on the above evaluation indicators, the BPNN model had the best performance, with an F1 score of 0.95, an accuracy rate of 94.87%, and an AUC of 0.9738.

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Truvace Impact Record TRV-2026-0975, v1: “Development of a Machine Learning Algorithm for Differential Diagnosis Between Primary Immune Thrombocytopenia and Connective Tissue Disease-Related Thrombocytopenia in Pediatric Patients.” Truvace, 2026-09-04. /record/TRV-2026-0975 (accessed at citation time). sha256 1c7177108638fe09…

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