摘要
目的:如今机器学习算法逐渐被应用于预测脑卒中和心血管疾病方面。与传统回归模型相比,机器学习可以通过探索大量预测特征与结果变量之间的灵活关系,从数据中学习,以实现高预测准确性,为个体化治疗和康复方案的制定提供了新的方法。此文旨在系统评价基于机器学习脑卒中功能恢复及预后的预测模型,综合评估其预测性能及临床应用潜力,为相关预后预测模型的构建、应用及推广提供参考。方法:按照PRISMA指南进行系统评价。通过检索PubMed、EMbase、Web of Science核心数据库、中国知网、万方和中国生物医学文献数据库,筛选出使用机器学习方法进行脑卒中预后预测的相关文献,检索时限为2014-01-01/2024-07-01。由2名研究人员严格按照纳入与排除标准独立筛选文献、提取数据,使用预测模型偏倚风险评价工具评价模型质量。结果:①初步检索共获取3126篇文献,经过筛选和排除,最终纳入18篇研究,共运用13种机器学习方法构建了150个预测模型,其中应用次数最多的3种方法为逻辑回归、随机森林和极限梯度提升(XGBoost);仅有1项研究开展了外部验证;有8项研究报告了缺失数据的处理方法;②结局指标方面有8项研究采用了临床数据与影像学数据结合来构建模型,9项研究仅运用临床数据构建模型,1项研究仅用影像学数据构建模型;③18项研究均给出了研究中最重要的特征,其中被提及最多的是美国国立卫生研究院卒中量表和年龄;所有研究均报告了曲线下面积值,范围0.74-0.96,最高为0.96;所有模型的总体偏倚风险均为高偏倚风险,模型分析领域高偏倚风险是导致所有模型总体偏倚风险高的主要原因;④Meta分析结果显示年龄和美国国立卫生研究院卒中量表评分对脑卒中预后影响显著,年龄[MD=8.49,95%CI(6.24,10.75),P<0.01],美国国立卫生研究院卒中量表评分[MD=4.78,95%CI(2.56,7.00),P<0.01]。结论:此次研究系统评价了基于机器学习的脑卒中功能恢复及预后预测模型,模型均具有良好的预测潜力。但未来研究应增加纳入模型样本量,采用前瞻性研究,并且添加对模型的外部验证以提高模型的稳定性和预测准确性,控制偏倚风险,以帮助模型在实际临床应用中的验证和推广,同时应对缺失值的插补更透明和精准。虽然现有的机器学习模型显示出良好的预测性能,但也要注重模型的功能性和可用性,纳入特征多会降低易用性。应开发简便易用的模型接口和用户友好的临床工具,使医护人员能够更好地应用模型进行临床决策。
OBJECTIVE:Nowadays,machine learning algorithms are gradually being applied to predict stroke and cardiovascular disease.Compared with traditional regression models,machine learning can learn from data to achieve high prediction accuracy by exploring the flexible relationship between a large number of predictive features and outcome variables,providing a new method for the formulation of individualized treatment and rehabilitation programs.This study aims to systematically evaluate stroke functional recovery and prognosis prediction models based on machine learning,comprehensively assessing their predictive performance and clinical application potential to provide references for the development,application,and promotion of related predictive models.METHODS:This review was conducted following the PRISMA(Preferred Reporting Items for Systematic Reviews and Meta-Analyses)guidelines.Relevant literature on stroke prognosis prediction using machine learning methods was selected by searching PubMed,EMbase,Web of Science Core Collection,CNKI,WanFang,and the China Biomedical Literature Database,with the search period from January 1,2014,to July 1,2024.Two researchers independently screened the literature and extracted data based on inclusion and exclusion criteria,using the Prediction model Risk Of Bias ASsessment Tool(PROBAST)to assess model quality.RESULTS:(1)A total of 3126 articles were obtained in the preliminary search.After screening and exclusion,18 articles were finally included.150 prediction models were constructed using 13 machine learning methods.The three most frequently used methods are Logistic Regression,Random Forest,and Extreme Gradient Boosting(XGBoost).Only one study was externally validated.Eight studies reported how the missing data were handled.(2)In terms of outcome indicators,8 studies used the combination of clinical data and imaging data to build models,9 studies only used clinical data to build models,and 1 study only used imaging data to build models.(3)Each of the 18 studies gave the most important characteristics of the study,with the most mentioned being the National Institute of Health Stroke Scale and age.All studies reported area under curve values ranging from 0.74 to 0.96,with the highest area under curve being 0.96.The overall risk of bias in all models was high.The high risk of bias in the field of model analysis was the main reason for the high risk of overall bias in all models.(4)The results of meta-analysis showed that age and National Institute of Health Stroke Scale score had significant influence on stroke prognosis,with age[MD=8.49,95%CI(6.24,10.75),P<0.01]and National Institute of Health Stroke Scale score[MD=4.78,95%CI(2.56,7.00),P<0.01].CONCLUSION:This study systematically evaluated the predictive model of functional recovery and prognosis of stroke based on machine learning,and all the models have good predictive potential.However,future studies should increase the sample size of the included model,adopt prospective studies,and add external validation of the model to improve the stability and prediction accuracy of the model,control the risk of bias,and contribute to the validation and promotion of the model in practical clinical applications.At the same time,the interpolation of missing values is more transparent and accurate.Although existing machine learning models show good predictive performance,it is also important to focus on the functionality and usability of the model,and the inclusion of features will reduce ease of use.We should develop easy to use model interfaces and user-friendly clinical tools to enable medical staff to better apply the model for clinical decision.
作者
王嘉孺
张瑛
杨永
祁文
肖华业
马秋平
杨连招
罗自维
何雅青
张江银
韦嘉雯
孟媛
谭思连
Wang Jiaru;Zhang Ying;Yang Yong;Qi Wen;Xiao Huaye;Ma Qiuping;Yang Lianzhao;Luo Ziwei;He Yaqing;Zhang Jiangyin;Wei Jiawen;Meng Yuan;Tan Silian(Guangxi University of Chinese Medicine,Nanning 530200,Guangxi Zhuang Autonomous Region,China;Faculty of Chinese Medicine Science,Guangxi University of Chinese Medicine,Nanning 530222,Guangxi Zhuang Autonomous Region,China;Guangxi Zhuang Autonomous Region Maternal and Child Health Hospital,Nanning 530021,Guangxi Zhuang Autonomous Region,China)
出处
《中国组织工程研究》
北大核心
2025年第29期6317-6325,共9页
Chinese Journal of Tissue Engineering Research
基金
2022年度广西高校中青年教师科研基础能力提升项目(自然科学类)(2022KY1670),项目负责人:张瑛
2022年广西中医药大学赛恩斯新医药学院校级科研项目(2022MS012),项目负责人:张瑛
2022年广西中医药大学校级科研项目(2022MS020),项目负责人:杨永
2024年广西中医药大学赛恩斯新医药学院校大学生创新训练计划项目(202413643028)(国家级),项目负责人:何雅青
广西中医药大学高层次人才创新培育团队(2022A010),项目负责人:马秋平
2020年广西哲学社会科学规划研究课题(20FGL024),项目负责人:杨连招
广西自然科学基金项目(2013GXNSFDA278001),项目负责人:杨连招。
关键词
机器学习
脑卒中
预后预测
功能恢复
系统评价
machine learning
stroke
prognosis prediction
functional recovery
systematic review
