ArticleClinical and applied thrombosis/hemostasis : official journal of the International Academy of Clinical and Applied Thrombosis/Hemostasis
A Machine Learning Approach to Predict Deep Venous Thrombosis Among Hospitalized Patients.
Article in Clinical and applied thrombosis/hemostasis : official journal of the International Academy of Clinical and Applied Thrombosis/Hemostasis. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 25 papers, 3 of them syntheses that pooled it.
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Who cites it
25 citing papers in PubMed, 3 syntheses or guidelines pooled it, 48 citations in OpenAlex.
- Machine Learning for Predicting Venous Thromboembolism After Joint Arthroplasty: Systematic Review of Clinical Applicability and Model Performance.JMIR medical informatics · 2026Pooled it
- Machine learning prediction models for deep vein thrombosis in hospitalized patients: a systematic review and meta-analysis.Frontiers in medicine · 2026Pooled it
- Machine Learning in the Prediction of Venous Thromboembolism: Systematic Review and Meta-Analysis.Journal of medical Internet research · 2025Pooled it
- Machine learning improves prediction of pulmonary thromboembolism and reduces unnecessary computed tomography scans in the emergency department.Scientific reports · 2026Article
- A machine learning-based risk prediction model for Hospitalized patients with deep vein thrombosis.PeerJ · 2026Article
- Machine Learning-Based Early Prediction of Lower Extremity Deep Vein Thrombosis in the ICU: A Multicenter Study.Risk management and healthcare policy · 2026Article
- Artificial intelligence in hematology: current trends and application areas.Annals of hematology · 2025Review
- Comparison of 7 artificial intelligence models in predicting venous thromboembolism in COVID-19 patients.Research and practice in thrombosis and haemostasis · 2025Article
- Predicting deep vein thrombosis using machine learning and blood routine analysis.Frontiers in big data · 2025Article
- Application of artificial intelligence in risk assessment and management of venous thromboembolism: scoping review.Frontiers in physiology · 2025Review
- Using machine learning models to predict post-revascularization thrombosis in PAD.Frontiers in artificial intelligence · 2025Article
- Incidence and Risk Factors of Lower Limb Deep Vein Thrombosis in Psychiatric Inpatients by Applying Machine Learning to Electronic Health Records: A Retrospective Cohort Study.Clinical epidemiology · 2025Article
- Efficient management of pulmonary embolism diagnosis using a two-step interconnected machine learning model based on electronic health records data.Health information science and systems · 2024Article
- Predicting Deep Venous Thrombosis Using Artificial Intelligence: A Clinical Data Approach.Bioengineering (Basel, Switzerland) · 2024Review
- Machine learning-based prediction model of lower extremity deep vein thrombosis after stroke.Journal of thrombosis and thrombolysis · 2024Article
- Prediction of Venous Thromboembolism in Diverse Populations Using Machine Learning and Structured Electronic Health Records.Arteriosclerosis, thrombosis, and vascular biology · 2024Article
- Machine Learning Approach for Improved Longitudinal Prediction of Progression from Mild Cognitive Impairment to Alzheimer's Disease.Diagnostics (Basel, Switzerland) · 2023Article
- Artificial Intelligence-Assisted Diagnostic Cytology and Genomic Testing for Hematologic Disorders.Cells · 2023Review
- Chinese experts' consensus on the application of intensive care big data.Frontiers in medicine · 2023Article
- Development and validation of machine learning models for venous thromboembolism risk assessment at admission: a retrospective study.Frontiers in cardiovascular medicine · 2023Article
Corrections and comments
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Authors and funding
9 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Deep venous thrombosis (DVT) is associated with significant morbidity, mortality, and increased healthcare costs. Standard scoring systems for DVT risk stratification often provide insufficient stratification of hospitalized patients and are unable to accurately predict which inpatients are most likely to present with DVT. There is a continued need for tools which can predict DVT in hospitalized patients. We performed a retrospective study on a database collected from a large academic hospital, comprised of 99,237 total general ward or ICU patients, 2,378 of whom experienced a DVT during their hospital stay. Gradient boosted machine learning algorithms were developed to predict a patient's risk of developing DVT at 12- and 24-hour windows prior to onset. The primary outcome of interest was diagnosis of in-hospital DVT. The machine learning predictors obtained AUROCs of 0.83 and 0.85 for DVT risk prediction on hospitalized patients at 12- and 24-hour windows, respectively. At both 12 and 24 hours before DVT onset, the most important features for prediction of DVT were cancer history, VTE history, and internal normalized ratio (INR). Improved risk stratification may prevent unnecessary invasive testing in patients for whom DVT cannot be ruled out using existing methods. Improved risk stratification may also allow for more targeted use of prophylactic anticoagulants, as well as earlier diagnosis and treatment, preventing the development of pulmonary emboli and other sequelae of DVT.
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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.