ArticleClinical and applied thrombosis/hemostasis : official journal of the International Academy of Clinical and Applied Thrombosis/Hemostasis
The Use of Machine Learning Techniques to Predict Deep Vein Thrombosis in Rehabilitation Inpatients.
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 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, 16 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 in the Prediction of Venous Thromboembolism: Systematic Review and Meta-Analysis.Journal of medical Internet research · 2025Pooled it
- Deep vein thrombosis as a public health priority: wHO's agenda for Africa 2030.Frontiers in cardiovascular medicine · 2026Review
- A machine learning-based risk prediction model for Hospitalized patients with deep vein thrombosis.PeerJ · 2026Article
- Machine Learning-Based Prediction of Postoperative Deep Vein Thrombosis Following Tibial Fracture Surgery.Diagnostics (Basel, Switzerland) · 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
- Machine learning-based prediction model of lower extremity deep vein thrombosis after stroke.Journal of thrombosis and thrombolysis · 2024Article
- Prevalence and Risk Factors of Deep Venous Thrombosis in Intensive Inpatient Neurorehabilitation Unit.Healthcare (Basel, Switzerland) · 2024Article
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Authors and funding
9 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundRehabilitation is crucial to recovering patients' dysfunction, improving their life quality, and promoting an early return to their family and society. In China, most patients in rehabilitation units are patients transferred from neurology, neurosurgery, and orthopedics, and most of these patients face problems such as continuously bedridden or varying degrees of limb dysfunction, all of which are risk factors for deep venous thrombosis. The formation of deep venous thrombosis can delay the recovery process and result in significant morbidity, mortality, and higher healthcare costs, so early detection and individualized treatment are needed. Machine learning algorithms can help develop more precise prognostic models, which can be of great significance in the development of rehabilitation training programs. In this study, we aimed to develop a model of deep venous thrombosis for inpatients in the Department of Rehabilitation Medicine at the Affiliated Hospital of Nantong University using machine learning methods.
methodsWe analyzed and compared 801 patients in the Department of Rehabilitation Medicine using machine learning. Support vector machine, logistic regression, decision tree, random forest classifier, and artificial neural network were used to build models.
resultsArtificial neural network was the better predictor than other traditional machine learnings. D-dimer levels, bedridden time, Barthel Index, and fibrinogen degradation products were common predictors of adverse outcomes in these models.
conclusionsRisk stratification can help healthcare practitioners to achieve improvements in clinical efficiency and specify appropriate rehabilitation training programs.
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