ArticleJournal of personalized medicine2022
Predicting Venous Thrombosis in Osteoarthritis Using a Machine Learning Algorithm: A Population-Based Cohort Study.
Article in Journal of personalized medicine, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.
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Who cites it
14 citing papers in PubMed, 19 citations in OpenAlex.
- Dynamic Ensemble Selection for Early Detection of Deep Vein Thrombosis in Fracture Patients.Journal of medical systems · 2026Article
- Development and validation of machine learning models for assessing the risk of postoperative venous thromboembolism in cervical cancer patients.Scientific reports · 2025Article
- A comprehensive review on cardiovascular and chronic kidney disease interplay, exploring biomarkers and algorithmic approaches in unraveling organ crosstalk.Indian journal of pharmacology · 2025Review
- Development and Validation of Machine Learning-Based Models for Prediction of Intensive Care Unit Admission and In-Hospital Mortality in Patients with Acute Exacerbations of Chronic Obstructive Pulmonary Disease.Chronic obstructive pulmonary diseases (Miami, Fla.) · 2024Article
- Development and evaluation of machine learning models for predicting large-for-gestational-age newborns in women exposed to radiation prior to pregnancy.BMC medical informatics and decision making · 2024Article
- Article
- Automating venous thromboembolism risk assessment: a dual-branch deep learning method using electronic medical records.Frontiers in medicine · 2023Article
- Development and validation of a machine learning model to predict venous thromboembolism among hospitalized cancer patients.Asia-Pacific journal of oncology nursing · 2022Article
- Explainable Machine Learning-Based Risk Prediction Model for In-Hospital Mortality after Continuous Renal Replacement Therapy Initiation.Diagnostics (Basel, Switzerland) · 2022Article
- Development and Evaluation of a Machine Learning Prediction Model for Small-for-Gestational-Age Births in Women Exposed to Radiation before Pregnancy.Journal of personalized medicine · 2022Article
- Explainable Machine Learning Model for Predicting First-Time Acute Exacerbation in Patients with Chronic Obstructive Pulmonary Disease.Journal of personalized medicine · 2022Article
- Predictive models for small-for-gestational-age births in women exposed to pesticides before pregnancy based on multiple machine learning algorithms.Frontiers in public health · 2022Article
- Application of artificial intelligence models for detecting the pterygium that requires surgical treatment based on anterior segment images.Frontiers in neuroscience · 2022Article
- Identification for heavy metals exposure on osteoarthritis among aging people and Machine learning for prediction: A study based on NHANES 2011-2020.Frontiers in public health · 2022Article
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Authors and funding
7 authors at 3 institutions in 1 country.
Funding
Abstract
Osteoarthritis (OA) is the most common joint disease associated with pain and disability. OA patients are at a high risk for venous thrombosis (VTE). Here, we developed an interpretable machine learning (ML)-based model to predict VTE risk in patients with OA. To establish a prediction model, we used six ML algorithms, of which 35 variables were employed. Recursive feature elimination (RFE) was used to screen the most related clinical variables associated with VTE. SHapley additive exPlanations (SHAP) were applied to interpret the ML mode and determine the importance of the selected features. Overall, 3169 patients with OA (average age: 66.52 ± 7.28 years) were recruited from Xi'an Honghui Hospital. Of these, 352 and 2817 patients were diagnosed with and without VTE, respectively. The XGBoost algorithm showed the best performance. According to the RFE algorithms, 15 variables were retained for further modeling with the XGBoost algorithm. The top three predictors were Kellgren-Lawrence grade, age, and hypertension. Our study showed that the XGBoost model with 15 variables has a high potential to predict VTE risk in patients with OA.
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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.