ArticleDisease markers2017
Validation of a Machine Learning Approach for Venous Thromboembolism Risk Prediction in Oncology.
Article in Disease markers, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 32 papers, 1 of them a synthesis that pooled it.
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Who cites it
32 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Artificial intelligence in the prediction of venous thromboembolism: A systematic review and pooled analysis.European journal of haematology · 2023Pooled it
- Development and validation of a prediction model of catheter-related thrombosis in patients with cancer undergoing chemotherapy based on ultrasonography results and clinical information.Journal of thrombosis and thrombolysis · 2022Trial
- 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
- 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
- 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
- Unveiling the potential of artificial intelligence in revolutionizing disease diagnosis and prediction: a comprehensive review of machine learning and deep learning approaches.European journal of medical research · 2025Review
- Scoring systems to predict thrombotic complications in solid tumor patients.Current opinion in hematology · 2025Review
- An Evaluation Benchmark for Adverse Drug Event Prediction from Clinical Trial Results.Scientific data · 2025Article
- In-house chromogenic anti-factor Xa assay: development, validation, and identification of factors predicting APTT discordance.Frontiers in medicine · 2025Article
- DNA liquid biopsy-based prediction of cancer-associated venous thromboembolism.Nature medicine · 2024Article
- Comparison of different machine learning classification models for predicting deep vein thrombosis in lower extremity fractures.Scientific reports · 2024Article
- Review
- Machine learning in cancer-associated thrombosis: hype or hope in untangling the clot.Bleeding, thrombosis and vascular biology · 2024Article
- Assessing the risk of venous thromboembolism in patients with haematological cancers using three prediction models.Journal of cancer research and clinical oncology · 2023Article
- Development and validation of machine learning models for venous thromboembolism risk assessment at admission: a retrospective study.Frontiers in cardiovascular medicine · 2023Article
- Review
- Cancer-associated venous thromboembolism.Nature reviews. Disease primers · 2022Review
- Burden of venous thromboembolism in patients with pancreatic cancer.World journal of gastroenterology · 2021Review
- Optimized CNNs to Indoor Localization through BLE Sensors Using Improved PSO.Sensors (Basel, Switzerland) · 2021Article
- The risk of venous thromboembolism in cancer patients receiving chemotherapy: a meta-analysis with systematic review.Annals of translational medicine · 2021Article
Corrections and comments
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Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Using kernel machine learning (ML) and random optimization (RO) techniques, we recently developed a set of venous thromboembolism (VTE) risk predictors, which could be useful to devise a web interface for VTE risk stratification in chemotherapy-treated cancer patients. This study was designed to validate a model incorporating the two best predictors and to compare their combined performance with that of the currently recommended Khorana score (KS). Age, sex, tumor site/stage, hematological attributes, blood lipids, glycemic indexes, liver and kidney function, BMI, performance status, and supportive and anticancer drugs of 608 cancer outpatients were all entered in the model, with numerical attributes analyzed as continuous values. VTE rate was 7.1%. The VTE risk prediction performance of the combined model resulted in 2.30 positive likelihood ratio (+LR), 0.46 negative LR (-LR), and 4.88 HR (95% CI: 2.54-9.37), with a significant improvement over the KS [HR 1.73 (95% CI: 0.47-6.37)]. These results confirm that a ML approach might be of clinical value for VTE risk stratification in chemotherapy-treated cancer outpatients and suggest that the ML-RO model proposed could be useful to design a web service able to provide physicians with a graphical interface helping in the critical phase of decision making.
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Registered trials
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.