基于机器学习的骨科术后深静脉血栓风险预测模型研究进展  

Research progress on risk prediction model for deep vein thrombosis after orthopedic surgery based on machine learning

作　　者：谈周 李春梅[1] 王豪 宋明芳 刘平芳 TAN Zhou;LI Chunmei;WANG Hao;SONG Mingfang;LIU Pingfang(Medical College of Jishou University,Hunan Province,Jishou416000,China;Department of Nursing,General Hospital of Hunan Medical College,Hunan Province,Huaihua418000,China)

机构地区：[1]吉首大学医学院,湖南吉首416000 [2]湖南医药学院总医院护理部,湖南怀化418000

出　　处：《中国医药导报》2025年第7期69-73,78,共6页China Medical Herald

基　　金：湖南省自然科学基金区域联合基金项目(2024JJ7345)。

摘　　要：深静脉血栓(DVT)在骨科术后的高发生率及其带来的严重后果,使有效预测和预防DVT成为骨科临床研究的重要课题。随着人工智能的快速发展,机器学习以其独特的优势在医学领域中崭露头角,通过整合多种资源,深度挖掘、分析大数据,为预测骨科术后DVT形成的风险提供有效手段。本文从机器学习和风险预测模型的概述、应用基础、在不同骨科手术中的应用现状、存在优势及局限性等多角度进行总结,旨在帮助医护人员识别潜在术后DVT风险患者,降低DVT发生率,为制订骨科术后DVT形成的个性化、规范化干预方案提供新思路。High incidence rate and serious consequences of deep vein thrombosis(DVT)after orthopedic surgery make effective prediction and prevention of DVT as an important topic in orthopedic clinical research.With rapid development of artificial intelligence,machine learning has emerged in medical field with its unique advantages,by integrating multiple resources,deeply mining and analyzing big data,provides effective means for predicting the risk of DVT after orthopedic surgery.This article summarizes overview of machine learning and risk prediction model,application foundation,application status in different orthopedic surgeries,advantages and limitations of existence from multiple perspectives,the aim is to help medical staff identify potential postoperative patients at risk of DVT,reduce incidence rate of DVT,provide new ideas for developing personalized and standardized intervention plans for DVT after orthopedic surgery.

关 键 词：骨科手术 深静脉血栓 机器学习 风险预测模型 

分 类 号：R687[医药卫生—骨科学]