Aug 2026
Machine learning-based prediction of peripherally inserted central catheter-related thrombosis in hematological malignancies: development and validation of a dynamic model in a multicenter cohort
The XGBoost model, integrating dynamic biomarkers and hematology-specific factors, provides superior personalized risk stratification for PICC-RT and enables the identification of high-risk patients who may benefit from enhanced surveillance or targeted thromboprophylaxis while safely identifying low-risk individuals.
Jian-Hong Su, Jun-Lin Hu, Wen-Tian Wang et al.
· Supportive Care in Cancer · 1 citation