{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,23]],"date-time":"2026-09-23T03:58:00Z","timestamp":1790135880761,"version":"4.0.1"},"reference-count":30,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2025,6,26]],"date-time":"2025-06-26T00:00:00Z","timestamp":1750896000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Med."],"abstract":"<jats:sec><jats:title>Objective<\/jats:title><jats:p>To construct a venous thromboembolism (VTE) risk prediction model for orthopedic inpatients using machine learning modeling techniques, identify high-risk patients, and optimize clinical interventions.<\/jats:p><\/jats:sec><jats:sec><jats:title>Methods<\/jats:title><jats:p>This study involved a retrospective analysis of 286 orthopedic inpatients from Nanxishan Hospital of Guangxi Zhuang Autonomous Region (The Second People\u2019s Hospital of Guangxi Zhuang Autonomous Region) from January 1, 2022 to December 31, 2022. To ensure patient information security, all data were fully anonymized before access. The collected data included basic information such as gender, age, ethnicity, and body mass index (BMI), lifestyle factors and medical history (including smoking, alcohol use, diabetes, hypertension, and personal and family history of VTE), clinical test results (such as thrombin time, plasma D-dimer, total bilirubin, and urinary protein via dry chemistry), as well as genetic test results related to VTE risk. Feature analysis and data mining were conducted, and eight different machine learning algorithms were used to build the prediction model. The SHapley Additive exPlanation (SHAP) method was used to rank the feature importance and explain the final model.<\/jats:p><\/jats:sec><jats:sec><jats:title>Results<\/jats:title><jats:p>Through a comprehensive evaluation and comparison of eight different machine learning models, the results clearly indicate that the XGBoost model outperforms the others across all performance metrics, achieving the highest accuracy of 0.828 and AUROC of 0.931, significantly surpassing the other models, particularly in prediction accuracy and discriminative ability. Compared to the traditional Caprini scoring model, XGBoost not only shows improvements in accuracy and specificity but also demonstrates a significant increase in Area Under the Curve (AUC), further validating its superior performance in VTE risk prediction.<\/jats:p><\/jats:sec><jats:sec><jats:title>Conclusion<\/jats:title><jats:p>This model can be effectively used for early risk prediction of VTE, helping to reduce the incidence of venous thromboembolism in orthopedic patients. Given its promising results, further validation and wider application of the model in clinical settings are warranted to enhance patient outcomes and improve preventive strategies.<\/jats:p><\/jats:sec>","DOI":"10.3389\/fmed.2025.1574546","type":"journal-article","created":{"date-parts":[[2025,6,26]],"date-time":"2025-06-26T05:27:59Z","timestamp":1750915679000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":3,"title":["A study on the risk prediction model for venous thromboembolism in orthopedic inpatients based on machine 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