ArticleHeliyon2023
Machine learning predicts cancer-associated venous thromboembolism using clinically available variables in gastric cancer patients.
Article in Heliyon, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
13 citing papers in PubMed, 24 citations in OpenAlex.
- A Systematic Evaluation of Cohort Selection Criteria and Their Impact on Machine Learning Model Performance and Demographic Disparities in COVID-19 Outcomes: Cohort Study.JMIR formative research · 2026Article
- A machine learning-based risk prediction model for Hospitalized patients with deep vein thrombosis.PeerJ · 2026Article
- Machine Learning-Driven Modeling to Predict Postdischarge Venous Thromboembolism After Pancreatectomy for Pancreas Cancer.Annals of surgical oncology · 2025Article
- Interpretable machine learning model for early prediction of disseminated intravascular coagulation in critically ill children.Scientific reports · 2025Article
- A Predictive Model Based on Machine Learning Algorithm for Vein Thrombosis After Ovarian Cancer Resection.International journal of women's health · 2025Article
- Development and validation of a predictive risk tool for VTE in women with breast cancer under chemotherapy: a cohort study in China.Breast cancer (Tokyo, Japan) · 2025Article
- Development and validation of a machine learning-based early warning system for predicting venous thromboembolism risk in hospitalized lymphoma patients undergoing chemotherapy: a multicenter and retrospective cohort study.Frontiers in oncology · 2025Article
- Analysis of vascular thrombus and clinicopathological factors in prognosis of gastric cancer: A retrospective cohort study.World journal of gastrointestinal oncology · 2024Article
- Predictive modeling of lower extreme deep vein thrombosis following radical gastrectomy for gastric cancer: based on multiple machine learning methods.Scientific reports · 2024Article
- Review
- Machine learning in cancer-associated thrombosis: hype or hope in untangling the clot.Bleeding, thrombosis and vascular biology · 2024Article
- Characteristics of fatal occupational injuries in migrant workers in South Korea: A machine learning study.Heliyon · 2023Article
- Development of a Predictive Model for Pulmonary Embolism in Gastrointestinal Cancer Patients with Isolated Distal Deep Vein Thrombosis.Clinical and applied thrombosis/hemostasis : official journal of the International Academy of Clinical and Applied Thrombosis/HemostasisArticle
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
9 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Stomach cancer (GC) has one of the highest rates of thrombosis among cancers and can lead to considerable morbidity, mortality, and additional costs. However, to date, there is no suitable venous thromboembolism (VTE) prediction model for gastric cancer patients to predict risk. Therefore, there is an urgent need to establish a clinical prediction model for VTE in gastric cancer patients. We collected data on 3092 patients between January 1, 2018 and December 31, 2021. And after feature selection, 11 variables are reserved as predictors to build the model. Five machine learning (ML) algorithms are used to build different VTE predictive models. The accuracy, sensitivity, specificity, and AUC of these five models were compared with traditional logistic regression (LR) to recommend the best VTE prediction model. RF and XGB models have selected the essential characters in the model: Clinical stage, Blood Transfusion History, D-Dimer, AGE, and FDP. The model has an AUC of 0.825, an accuracy of 0.799, a sensitivity of 0.710, and a specificity of 0.802 in the validation set. The model has good performance and high application value in clinical practice, and can identify high-risk groups of gastric cancer patients and prevent venous thromboembolism.
Indexed as
Identifiers
What Socratic holds
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 Socratic graph.