Observational studyJournal of nursing scholarship : an official publication of Sigma Theta Tau International Honor Society of Nursing2021
Personalized Risk Prediction for 30-Day Readmissions With Venous Thromboembolism Using Machine Learning.
Observational study in Journal of nursing scholarship : an official publication of Sigma Theta Tau International Honor Society of Nursing, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed.
- Application of machine learning models in predicting the risk of thromboembolic events in patients with nonvariceal gastrointestinal bleeding.World journal of gastroenterology · 2026Article
- Advancements in Acute Pulmonary Embolism Diagnosis and Treatment: A Narrative Review of Emerging Imaging Techniques and Intravascular Interventions.Journal of cardiovascular development and disease · 2025Review
- Role of Artificial Intelligence in the Diagnosis and Management of Pulmonary Embolism: A Comprehensive Review.Diagnostics (Basel, Switzerland) · 2025Review
- Application of artificial intelligence in risk assessment and management of venous thromboembolism: scoping review.Frontiers in physiology · 2025Review
- Automating venous thromboembolism risk assessment: a dual-branch deep learning method using electronic medical records.Frontiers in medicine · 2023Article
- Venous thromboembolism risk assessment of surgical patients in Southwest China using real-world data: establishment and evaluation of an improved venous thromboembolism risk model.BMC medical informatics and decision making · 2022Article
Corrections and comments
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Authors and funding
5 authors.
Funding
Abstract
purposeThe aim of the study was to develop and validate machine learning models to predict the personalized risk for 30-day readmission with venous thromboembolism (VTE).
designThis study was a retrospective, observational study.
methodsWe extracted and preprocessed the structured electronic health records (EHRs) from a single academic hospital. Then we developed and evaluated three prediction models using logistic regression, the balanced random forest model, and the multilayer perceptron.
resultsThe study sample included 158,804 total admissions; VTE-positive cases accounted for 2,080 admissions from among 1,695 patients (1.31%). Based on the evaluation results, the balanced random forest model outperformed the other two risk prediction models.
conclusionsThis study delivered a high-performing, validated risk prediction tool using machine learning and EHRs to identify patients at high risk for VTE after discharge. CLINICAL RELEVANCE: The risk prediction model developed in this study can potentially guide treatment decisions for discharged patients for better patient outcomes.
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