Machine learning-based prediction of central line-associated bloodstream infection in children with acute leukaemia.
AI interpretation is pending for this paper.
Open original publication →What the AI sees
Not AI summarized yet.
Research significance
Pending deeper interpretation.
Source abstract
BACKGROUND: Central line-associated bloodstream infection (CLABSI) is a common and serious complication in children with acute leukaemia (AL), leading to increased morbidity, prolonged hospitalization, and adverse clinical outcomes. Early identification of patients at high risk of CLABSI remains a major clinical challenge. AIM: To develop and evaluate machine learning models for early prediction of CLABSI risk in paediatric patients with AL and to identify key clinical factors associated with infection. METHODS: A retrospective study was conducted using clinical data from 407 paediatric patients with AL. Clinical variables were collected and preprocessed for model development. Six machine learning algorithms were constructed and compared for CLABSI prediction. Model performance was evaluated using standard classification metrics, and feature importance analysis was performed to identify major predictors associated with CLABSI. FINDINGS: Among the evaluated models, the Tabular Prior-Fitted Network (TabPFN) achieved the best predictive performance, with an accuracy of 91.2%. Feature importance analysis indicated that corticosteroid type, body temperature, neutrophil count, and white blood cell count were the most influential factors associated with CLABSI risk. The results demonstrated that machine learning models can effectively distinguish patients at high risk of infection. CONCLUSION: Machine learning-based prediction models show considerable potential for early CLABSI risk assessment in paediatric patients with AL. The proposed approach may support clinical decision-making and facilitate timely preventive interventions. Further multicentre studies are required to validate the model and assess its generalizability in broader clinical settings.