ArticleFrontiers in cardiovascular medicine2022
Development and Validation of a Risk Prediction Model for Venous Thromboembolism in Lung Cancer Patients Using Machine Learning.
Article in Frontiers in cardiovascular medicine, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers, 2 of them syntheses that pooled it.
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Who cites it
20 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Machine Learning in the Prediction of Venous Thromboembolism: Systematic Review and Meta-Analysis.Journal of medical Internet research · 2025Pooled it
- Incidence and risk factors of VTE in lung cancer: a meta-analysis.Annals of medicine · 2024Pooled it
- Development and validation of venous thrombosis risk prediction model and scale for lung cancer patients in intensive care unit based on interpretable machine learning.Journal of thoracic disease · 2026Article
- Development and validation of the IVENUS artificial intelligence model for automated CEAP classification of early-stage chronic venous disease using lower limb photographs.Journal of vascular surgery. Venous and lymphatic disorders · 2026Article
- Construction and verification of a nomogram to predict the probability of venous thromboembolism in lung cancer patients.Frontiers in molecular biosciences · 2026Article
- Development, validation, and clinical utility of risk prediction models for cancer-associated venous thromboembolism: A retrospective and prospective cohort study.Asia-Pacific journal of oncology nursing · 2025Article
- Predicting survival benefits of immune checkpoint inhibitor therapy in lung cancer patients: a machine learning approach using real-world data.International journal of clinical pharmacy · 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
- A nomogram predicting venous thromboembolism risk in primary liver cancer patients.Journal of thrombosis and thrombolysis · 2025Article
- Establishment and validation of a convenient and efficient screening tool for active pulmonary tuberculosis in lung cancer patients based on common parameters.Therapeutic advances in medical oncology · 2025Article
- Predicting a failure of postoperative thromboprophylaxis in non-small cell lung cancer: A stacking machine learning approach.PloS one · 2025Article
- Identification of key risk factors for venous thromboembolism in urological inpatients based on the Caprini scale and interpretable machine learning methods.Thrombosis journal · 2024Article
- A Random Forest Algorithm for Assessing Risk Factors Associated With Chronic Kidney Disease: Observational Study.Asian/Pacific Island nursing journal · 2024Article
- Review
- Construction and validation of risk prediction models for pulmonary embolism in hospitalized patients based on different machine learning methods.Frontiers in cardiovascular medicine · 2024Article
- Machine learning in cancer-associated thrombosis: hype or hope in untangling the clot.Bleeding, thrombosis and vascular biology · 2024Article
- Nomogram prediction for the risk of venous thromboembolism in patients with lung cancer.Cancer cell international · 2023Article
- Three non-small cell lung cancer patients who developed pulmonary thromboses during osimertinib treatment and could safely resume concomitant anticoagulation treatment: a report of three cases.Translational lung cancer research · 2022Article
- Development and validation of a machine learning model to predict venous thromboembolism among hospitalized cancer patients.Asia-Pacific journal of oncology nursing · 2022Article
- Semi-Supervised Learning to Improve Generalizability of Cancer Associated-Venous Thromboembolism Risk Prediction Models.Clinical and applied thrombosis/hemostasis : official journal of the International Academy of Clinical and Applied Thrombosis/HemostasisArticle
Corrections and comments
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15 authors.
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Abstract
Background: There is currently a lack of model for predicting the occurrence of venous thromboembolism (VTE) in patients with lung cancer. Machine learning (ML) techniques are being increasingly adapted for use in the medical field because of their capabilities of intelligent analysis and scalability. This study aimed to develop and validate ML models to predict the incidence of VTE among lung cancer patients. Methods: Data of lung cancer patients from a Grade 3A cancer hospital in China with and without VTE were included. Patient characteristics and clinical predictors related to VTE were collected. The primary endpoint was the diagnosis of VTE during index hospitalization. We calculated and compared the area under the receiver operating characteristic curve (AUROC) using the selected best-performed model (Random Forest model) through multiple model comparison, as well as investigated feature contributions during the training process with both permutation importance scores and the impurity-based feature importance scores in random forest model. Results: In total, 3,398 patients were included in our study, 125 of whom experienced VTE during their hospital stay. The ROC curve and precision-recall curve (PRC) for Random Forest Model showed an AUROC of 0.91 (95% Conclusion: According to the study results, there was no conspicuous decrease in the model's performance when use fewer features to predict, we concluded that our simplified model would be more applicable in real-life clinical settings. The developed model using ML algorithms in our study has the potential to improve the early detection and prediction of the incidence of VTE in patients with lung cancer.
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