biostudies-literatureLi Hministry of science and technology of the people's republic of chinanational natural science foundation of china18https://www.ebi.ac.uk/biostudies/studies/S-EPMC9004113biostudies-literatureUnknown20(1)<h4>Background</h4>An increase in the incidence of central venous catheter (CVC)-related thrombosis (CRT) has been reported in pediatric intensive care patients over the past decade. Risk factors for the development of CRT are not well understood, especially in children. The study objective was to identify potential clinical risk factors associated with CRT with novel fusion machine learning models.<h4>Methods</h4>Patients aged 0-18 who were admitted to intensive care units from December 2015 to December 2018 and underwent at least one CVC placement were included. Two fusion model approaches (stacking and blending) were used to build a better performance model based on three widely used machine learning models (logistic regression, random forest and gradient boosting decision tree). High-iThrombosis journalRisk factors for central venous catheter-associated deep venous thrombosis in pediatric critical care settings identified by fusion model.PMC90041132016YFC090190581871456Li HFu CShu QZhu JLu YFeng YZeng XDuan HfalseRisk factors for central venous catheter-associated deep venous thrombosis in pediatric critical care settings identified by fusion model.<h4>Background</h4>An increase in the incidence of central venous catheter (CVC)-related thrombosis (CRT) has been reported in pediatric intensive care patients over the past decade. Risk factors for the development of CRT are not well understood, especially in children. The study objective was to identify potential clinical risk factors associated with CRT with novel fusion machine learning models.<h4>Methods</h4>Patients aged 0-18 who were admitted to intensive care units from December 2015 to December 2018 and underwent at least one CVC placement were included. Two fusion model approaches (stacking and blending) were used to build a better performance model based on three widely used machine learning models (logistic regression, random forest and gradient boosting decision tree). High-i2022-01-01T00:00:00Z2022 Apr2025-04-26T07:28:06.731Z2025-04-06T12:19:54.245ZS-EPMC90041133541408610.1186/s12959-022-00378-y