机器学习在骨科静脉血栓栓塞症风险预测模型中的应用研究进展

Research progress on the application of machine learning in predictive modeling of venous thromboembolism risk in orthopedics

静脉血栓栓塞症是骨科最常见的并发症之一。近年来,机器学习广泛应用于骨科,机器学习其本质是运用算法对大量数据进行分析,构建风险预测模型预测未知临床结局发生的风险。机器学习通过将高危风险因素整合,帮助医务人员精准识别和筛选出静脉血栓栓塞症高危人群,为临床医务人员及时给予个体化干预措施提供科学依据。本文概述了机器学习的概念和分类、机器学习提高模型预测性能方面的优势以及机器学习在静脉血栓栓塞症患者构建风险预测模型的应用现状。

Venous thromboembolism(VTE)is a prevalent complication in orthopedics.In recent years,machine learning has been widely applied in orthopedics.The essence of machine learning lies in utilizing algorithms to analyze vast amounts of data and construct risk prediction models that can accurately forecast unknown clinical outcomes.By integrating high-risk factors,machine learning aids medical professionals in precisely identifying and screening individuals with a high risk of VTE and offering them timely individualized interventions.This article reviews the concept and classification of machine learning,the advantages of machine learning in enhancing model prediction ability,and the current application status of machine learning in constructing risk prediction models for patients with VTE.