ArticleScientific reports2024
Comparison of different machine learning classification models for predicting deep vein thrombosis in lower extremity fractures.
Article in Scientific reports, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 papers, 5 of them syntheses that pooled it.
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Who cites it
21 citing papers in PubMed, 5 syntheses or guidelines pooled it, 29 citations in OpenAlex.
- Artificial intelligence for venous thromboembolism risk stratification in surgical patients: a systematic review.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
- Machine learning prediction models for deep vein thrombosis in hospitalized patients: a systematic review and meta-analysis.Frontiers in medicine · 2026Pooled it
- The risk prediction models for deep venous thrombosis in perioperative patients with lower limb fractures: a systematic review and meta-analysis.BMC musculoskeletal disorders · 2025Pooled it
- Machine Learning in the Prediction of Venous Thromboembolism: Systematic Review and Meta-Analysis.Journal of medical Internet research · 2025Pooled it
- Development, internal testing, and external validation of an interpretable machine-learning model for predicting imaging-confirmed in-hospital postoperative deep vein thrombosis in older patients with patellar fractures.BMC medical informatics and decision making · 2026Article
- Integrated multi-omics and machine learning identify an interaction between SLC39A11 and phosphoinositide metabolism in deep vein thrombosis.BMC medical informatics and decision making · 2026Article
- Article
- Machine Learning Model Predicts New-Onset Lower Extremity Deep Vein Thrombosis After Pelvic Fracture Surgery and Targeted Diagnosis.Clinical epidemiology · 2026Article
- Prediction of carotid vulnerable plaques in patients with type 2 diabetes using interpretable machine learning models.Frontiers in endocrinology · 2026Article
- Machine learning prediction of deep vein thrombosis before staged surgery in lower-leg and ankle fractures treated with temporary external fixation.Frontiers in cardiovascular medicine · 2026Article
- Preoperative deep vein thrombosis in tibial plateau fractures: development and internal validation of an interpretable multivariable machine-learning diagnostic model.Frontiers in medicine · 2026Article
- Machine Learning-Based Prediction of Postoperative Deep Vein Thrombosis Following Tibial Fracture Surgery.Diagnostics (Basel, Switzerland) · 2025Article
- Predicting Preoperative Deep Vein Thrombosis in Elderly Hip Fracture Patients Using an Interpretable Machine Learning Model.International journal of general medicine · 2025Article
- Predicting deep vein thrombosis using machine learning and blood routine analysis.Frontiers in big data · 2025Article
- Integration of bulk RNA-seq and scRNA-seq reveals transcriptomic signatures associated with deep vein thrombosis.Frontiers in genetics · 2025Article
- Normative isokinetic knee strength values and prediction models in non-athletic Chinese adults.Frontiers in bioengineering and biotechnology · 2025Article
- Application of artificial intelligence in risk assessment and management of venous thromboembolism: scoping review.Frontiers in physiology · 2025Review
- Predicting Deep Venous Thrombosis Using Artificial Intelligence: A Clinical Data Approach.Bioengineering (Basel, Switzerland) · 2024Review
- Prediction model of deep vein thrombosis risk after lower extremity orthopedic surgery.Heliyon · 2024Article
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Authors and funding
8 authors at 2 institutions in 1 country.
Funding
Abstract
Deep vein thrombosis (DVT) is a common complication in patients with lower extremity fractures. Once it occurs, it will seriously affect the quality of life and postoperative recovery of patients. Therefore, early prediction and prevention of DVT can effectively improve the prognosis of patients. This study constructed different machine learning models to explore their effectiveness in predicting DVT. Five prediction models were applied to the study, including Extreme Gradient Boosting (XGBoost) model, Logistic Regression (LR) model, RandomForest (RF) model, Multilayer Perceptron (MLP) model, and Support Vector Machine (SVM) model. Afterwards, the performance of the obtained prediction models was evaluated by area under the curve (AUC), accuracy, sensitivity, specificity, F1 score, and Kappa. The prediction performances of the models based on machine learning are as follows: XGBoost model (AUC = 0.979, accuracy = 0.931), LR model (AUC = 0.821, accuracy = 0.758), RF model (AUC = 0.970, accuracy = 0.921), MLP model (AUC = 0.830, accuracy = 0.756), SVM model (AUC = 0.713, accuracy = 0.661). On our data set, the XGBoost model has the best performance. However, the model still needs external verification research before clinical application.
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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.