ArticleCritical care (London, England)2023
Interpretable machine learning models for predicting venous thromboembolism in the intensive care unit: an analysis based on data from 207 centers.
Article in Critical care (London, England), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 55 papers, 2 of them syntheses that pooled it.
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Who cites it
55 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Artificial intelligence in computational modeling of thrombosis: Bridging mechanistic insights and clinical translation.Journal of thrombosis and thrombolysis · 2026Pooled it
- Machine Learning for Predicting Venous Thromboembolism After Joint Arthroplasty: Systematic Review of Clinical Applicability and Model Performance.JMIR medical informatics · 2026Pooled it
- Integrating artificial intelligence into venous thromboembolism care: Predictive models, implementation challenges, and future directions.Journal of vascular surgery. Venous and lymphatic disorders · 2026Article
- MCD biomarkers Egfr, Hmox1, Lgmn identified in NAFLD.BMC endocrine disorders · 2026Article
- Multi-omics analysis identifies GINS1 as a prognostic biomarker in lung adenocarcinoma, linked to macrophage polarization and tumor cell survival.Discover oncology · 2026Article
- An interpretable machine-learning model for post-admission reassessment of bloodstream infection risk in ICU patients with pneumonia.Scientific reports · 2026Article
- Assessment of Venous Thromboembolism Risk Among Patients Admitted to Intensive Care Units in Dhaka City: A Cross-Sectional Study.Public health challenges · 2026Article
- A deep learning model to dynamically predict cancer-associated thromboembolism in large-scale healthcare systems.NPJ digital medicine · 2026Article
- Machine learning risk prediction models for medication harm in hospitalised adult patients.Therapeutic advances in drug safety · 2026Article
- Identification of Potential Biomarkers and Drugs for Papillary Thyroid Carcinoma Using Computational Analysis and Molecular Docking.Current medicinal chemistry · 2026Article
- Exploration of potential mechanisms and biomarkers related to ERS-associated RCD in steroid-induced osteonecrosis of the femoral head based on bioinformatics, with experimental validation.Frontiers in endocrinology · 2026Article
- Development and validation of an explainable machine learning model for predicting postoperative venous thromboembolism in esophageal cancer.International journal of surgery (London, England) · 2026Article
- Explainable machine learning for predicting venous thromboembolism in septic shock patients.Frontiers in immunology · 2026Article
- Machine Learning-Based Early Prediction of Lower Extremity Deep Vein Thrombosis in the ICU: A Multicenter Study.Risk management and healthcare policy · 2026Article
- Prognostic significance of inflammatory and nutritional indicators for treatment outcomes in untreated tuberculosis patients with hypertension.Frontiers in nutrition · 2026Article
- Interpretable machine learning for prognostic prediction in critically ill patients with coronary artery disease: a multicenter study.Frontiers in medicine · 2026Article
- A machine learning-based risk prediction model for Hospitalized patients with deep vein thrombosis.PeerJ · 2026Article
- Development and validation of an interpretable machine learning-based model for predicting carbapenem-resistantFrontiers in cellular and infection microbiology · 2026Article
- Multi-omics and spatial transcriptomics decode the ZDHHC9-driven hypoxia-immunosuppressive axis in hepatocellular carcinoma.Frontiers in oncology · 2026Article
- Development and external validation of a nomogram to predict the need for electrical cardioversion during radiofrequency ablation in patients with atrial fibrillation.International journal of cardiology. Heart & vasculature · 2025Article
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4 authors.
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Abstract
backgroundVenous thromboembolism (VTE) is a severe complication in critically ill patients, often resulting in death and long-term disability and is one of the major contributors to the global burden of disease. This study aimed to construct an interpretable machine learning (ML) model for predicting VTE in critically ill patients based on clinical features and laboratory indicators.
methodsData for this study were extracted from the eICU Collaborative Research Database (version 2.0). A stepwise logistic regression model was used to select the predictors that were eventually included in the model. The random forest, extreme gradient boosting (XGBoost) and support vector machine algorithms were used to construct the model using fivefold cross-validation. The area under curve (AUC), accuracy, no information rate, balanced accuracy, kappa, sensitivity, specificity, precision, and F1 score were used to assess the model's performance. In addition, the DALEX package was used to improve the interpretability of the final model.
resultsThis study ultimately included 109,044 patients, of which 1647 (1.5%) had VTE during ICU hospitalization. Among the three models, the Random Forest model (AUC: 0.9378; Accuracy: 0.9958; Kappa: 0.8371; Precision: 0.9095; F1 score: 0.8393; Sensitivity: 0.7791; Specificity: 0.9989) performed the best.
conclusionML models can be a reliable tool for predicting VTE in critically ill patients. Among all the models we had constructed, the random forest model was the most effective model that helps the user identify patients at high risk of VTE early so that early intervention can be implemented to reduce the burden of VTE on the patients.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.