ArticleComputers in biology and medicine2021
Predicting presumed serious infection among hospitalized children on central venous lines with machine learning.
Article in Computers in biology and medicine, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.
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Who cites it
15 citing papers in PubMed.
- Article
- Performance Degradation between Development and Deployment of a Predictive Model for Central Line-Associated Bloodstream Infections in Hospitalized Children.Applied clinical informatics · 2025Article
- A common data model for the standardization of intensive care unit medication features.JAMIA open · 2024Article
- Machine learning applications on neonatal sepsis treatment: a scoping review.BMC infectious diseases · 2023Article
- A natural language processing approach to categorise contributing factors from patient safety event reports.BMJ health & care informatics · 2023Article
- Age-based centiles for diastolic blood pressure among children in the out-of-hospital emergency setting.Journal of the American College of Emergency Physicians open · 2023Article
- Integrating structured and unstructured data for timely prediction of bloodstream infection among children.Pediatric research · 2023Article
- Recognition of outer membrane proteins using multiple feature fusion.Frontiers in genetics · 2023Article
- Developing machine learning systems worthy of trust for infection science: a requirement for future implementation into clinical practice.Frontiers in digital health · 2023Review
- Prediction of apoptosis protein subcellular location based on amphiphilic pseudo amino acid composition.Frontiers in genetics · 2023Article
- Prediction of Lumbar Drainage-Related Meningitis Based on Supervised Machine Learning Algorithms.Frontiers in public health · 2022Article
- DeepMC-iNABP: Deep learning for multiclass identification and classification of nucleic acid-binding proteins.Computational and structural biotechnology journal · 2022Article
- Predicting bloodstream infection outcome using machine learning.Scientific reports · 2021Article
- Improvement of APACHE II score system for disease severity based on XGBoost algorithm.BMC medical informatics and decision making · 2021Article
- Deep Learning Model to Predict Serious Infection Among Children With Central Venous Lines.Frontiers in pediatrics · 2021Article
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Authors and funding
7 authors.
Funding
Abstract
backgroundPresumed serious infection (PSI) is defined as a blood culture drawn and new antibiotic course of at least 4 days among pediatric patients with Central Venous Lines (CVLs). Early PSI prediction and use of medical interventions can prevent adverse outcomes and improve the quality of care.
methodsClinical features including demographics, laboratory results, vital signs, characteristics of the CVLs and medications used were extracted retrospectively from electronic medical records. Data were aggregated across all hospitals within a single pediatric health system and used to train machine learning models (XGBoost and ElasticNet) to predict the occurrence of PSI 8 h prior to clinical suspicion. Prediction for PSI was benchmarked against PRISM-III.
resultsOur model achieved an area under the receiver operating characteristic curve of 0.84 (95% CI = [0.82, 0.85]), sensitivity of 0.73 [0.69, 0.74], and positive predictive value (PPV) of 0.36 [0.34, 0.36]. The PRISM-III conversely achieved a lower sensitivity of 0.19 [0.16, 0.22] and PPV of 0.30 [0.26, 0.34] at a cut-off of ≥ 10. The features with the most impact on the PSI prediction were maximum diastolic blood pressure prior to PSI prediction (mean SHAP = 3.4), height (mean SHAP = 3.2), and maximum temperature prior to PSI prediction (mean SHAP = 2.6).
conclusionA machine learning model using common features in the electronic medical records can predict the onset of serious infections in children with central venous lines at least 8 h prior to when a clinical team drew a blood culture.
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