ArticleJournal of healthcare informatics research2022
A Machine Learning Framework for Assessing the Risk of Venous Thromboembolism in Patients Undergoing Hip or Knee Replacement.
Article in Journal of healthcare informatics research, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers, 2 of them syntheses that pooled it.
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Who cites it
9 citing papers in PubMed, 2 syntheses or guidelines pooled it, 15 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
- Hidden Preoperative Lower-Extremity Venous Thrombosis in Pelvic Organ Prolapse Surgery: Prevalence and Development of a Clinical Prediction Model.International urogynecology journal · 2026Article
- Risk factors for venous thromboembolism after knee arthroscopy: a systematic review and meta-analysis.Thrombosis journal · 2025Review
- Determination of Patient-Specific Blood Coagulation Kinetic Parameters via Neural Networks: Toward Thrombosis Prediction in Personalized Medicine.Annals of biomedical engineering · 2025Article
- The Prediction of Venous Thromboembolism Using Artificial Intelligence and Machine Learning in Lower Extremity Arthroplasty: A Systematic Review.Arthroplasty today · 2025Article
- Predicting 30-day reoperation following primary total knee arthroplasty: machine learning model outperforms the ACS risk calculator.Medical & biological engineering & computing · 2025Article
- Predicting 30-Day Venous Thromboembolism Following Total Joint Arthroplasty: Adjusting for Trends in Annual Length of Stay.Arthroplasty today · 2024Article
- Prediction model of deep vein thrombosis risk after lower extremity orthopedic surgery.Heliyon · 2024Article
Corrections and comments
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Authors and funding
4 authors at 3 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Venous thromboembolism (VTE) is a well-recognized complication that is prevalent in patients undergoing major orthopedic surgery (e.g., total hip arthroplasty and total knee arthroplasty). For years, to identify patients at high risk of developing VTE, physicians have relied on traditional risk scoring systems, which are too simplistic to capture the risk level accurately. In this paper, we propose a data-driven machine learning framework to identify such high-risk patients before they undergo a major hip or knee surgery. Using electronic health records of more than 392,000 patients who undergone a major orthopedic surgery, and following a guided feature selection using the genetic algorithm, we trained a fully connected deep neural network model to predict high-risk patients for developing VTE. We identified several risk factors for VTE that were not previously recognized. The best FCDNN model trained using the selected features yielded an area under the ROC curve (AUC) of 0.873, which was remarkably higher than the best AUC obtained by including only risk factors previously known in the medical literature. Our findings suggest several interesting and important insights. The traditional risk scoring tables that are being widely used by physicians to identify high-risk patients are not considering a comprehensive set of risk factors, nor are they as powerful as cutting-edge machine learning methods in distinguishing low- from high-risk patients.
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Registered trials
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