护理学报2023，Vol.30Issue(23)：44-50,7.DOI:10.16460/j.issn1008-9969.2023.23.044
基于机器学习构建住院患者深静脉血栓风险预测模型的系统评价
Machine learning-based a risk predictive model for deep vein thrombosis in hospitalized patients:a systematic review
摘要
Abstract
Objective To systematically evaluate the risk prediction model for deep vein thrombosis in hospitalized patients based on machine learning.Methods We conducted literature research in PubMed,Embase,CHINHAL,Cochrane Library,Web of Science,CNKI,and Wanfang databases for literature on risk prediction models for deep vein thrombosis in hospitalized patients constructed by machine learning.The search period spanned from the inception to March 2023.Two researchers completed literature screening and data extraction independently,and used predictive models to construct a research data extraction and quality evaluation checklist(CHARMS)to evaluate the quality of the included literature and screened high-quality literature for discussion.Results Totally 11 high-quality studies were collected,including 28 machine learning models,with an area under the ROC curve ranging from 0.710 to 0.976.Laboratory indicators such as age,VTE history,length of hospital stay,medication history,and D-dimer were are the main predictive factors.Conclusions Risk prediction models constructed using machine learning can accurately identify the risk of DVT events in hospitalized patients,and its predictive performance is superior to traditional risk prediction models.The available literature on the topic exhibits a low overall risk of bias,however,the applicability level of the prediction model is considered average.关键词
深静脉血栓/DVT/机器学习/护理/预测模型/系统评价Key words
Deep vein thrombosis/DVT/machine learning/nursing/predictive model/systematic review分类
医药卫生引用本文复制引用
杨楠楠,蒋慧萍,史婷奇..基于机器学习构建住院患者深静脉血栓风险预测模型的系统评价[J].护理学报,2023,30(23):44-50,7.基金项目
南京市卫生科技发展专项资金项目(YKK22074) （YKK22074）
