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
Aug 2026· Supportive Care in Cancer· Vol 34· 1 citation· 46 references
Medicine
TL;DR
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.
Machine learning models demonstrated favorable performance for predicting in-hospital DVT after AIS, and SVM showed the most favorable overall performance, whereas XGBoost prioritized sensitivity.
Tie-Shi Zhu, Run-Zhui Lin, Le Zhao et al.· Frontiers in Neurology· 0 citations
The XGBoost model showed potential for DVT risk assessment in patients with AECOPD, and SCI, as an integrated marker of inflammation and coagulation, may provide complementary information for risk stratification.
Li Zhou, Hui Wang, Di Yao et al.· International Journal of Gen...· 0 citations
The LGBM model constructed based on the MIMIC-IV database has excellent predictive efficacy for VTE after orthopedic surgery and certain external generalization abilities, providing a reference for the early identification of high-risk individuals in clinical settings.
Gen-Fa Wu, Geng-Jiao Cai, Gang Wu et al.· Frontiers in Surgery· 0 citations
Abstract Background Deep vein thrombosis (DVT) is a major cardiovascular burden, and existing risk-stratification tools often rely on laboratory biomarkers, limiting their utility in primary care. We developed and externally validated a simplified, laboratory-free nomogram to predict DVT in community settings. Methods...
Background Sepsis-induced cardiomyopathy (SICM) is a common complication of sepsis and is associated with poor short-term outcomes. However, effective tools for early risk stratification remain limited. This study aimed to develop a machine learning (ML)–based model to predict 28-day mortality in patients with SICM. Me...
Chun Yang, Jing-Yu Wang, Jia-Liang Li et al.· Journal of Inflammation Rese...· 0 citations
This model provides a reference for surgical risk stratification, which helps establish personalized postoperative monitoring strategies and demonstrates an interpretable machine learning model to effectively predict POF following fURL.
Xiao-Fei Lu, Chun-Ping Yu, Zhi-Yong Ding et al.· Frontiers in Medicine· 0 citations
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