中医康复2025，Vol.2Issue(1)：12-17,6.DOI:10.19787/j.issn.2097-3128.2025.01.003
机器学习模型在亚急性期脑卒中患者康复后功能结局预测中的价值研究
The Value of Machine Learning Models in Predicting Functional Outcomes after Rehabilitation in Patients with Subacute Stroke
摘要
Abstract
Objective:This study aims to evaluate the efficacy of machine learning models compared to stepwise linear regression(SLR)in predicting functional outcomes following rehabilitation in patients with subacute stroke.Methods:A total of 1,046 subacute stroke patients admitted to the 945th Hospital of the Joint Logistics Support Force from January 2013 to December 2023 were in-cluded in this study.Patient demographics and Functional Independence Measure(FIM)scores at admission were used to construct various predictive models including SLR,Regression Trees(RT),Ensemble Learning(EL),Artificial Neural Networks(ANN),Sup-port Vector Regression(SVR),and Gaussian Process Regression(GPR).These models were evaluated using 10-fold cross-validation to compare the actual and predicted discharge FIM scores and the coefficients of determination(R2)and Root Mean Squared Error(RMSE)of FIM gains.Results:Machine learning models demonstrated superior performance in predicting FIM motor scores(R2:RT=0.75,EL=0.78,ANN=0.81,SVR=0.80,GPR=0.81)compared to SLR(0.70).These models also showed higher accuracy in predict-ing total FIM gain scores(R2:RT=0.48,EL=0.51,ANN=0.50,SVR=0.51,GPR=0.54)than SLR(0.22).Conclusion:Machine learn-ing models outperform SLR in predicting FIM outcomes.The accuracy of predictions using only patient demographics and admis-sion FIM scores in machine learning models was superior to previous studies,with GPR showing the highest predictive accuracy for FIM outcomes.关键词
脑卒中/亚急性期/机器学习/预测/功能独立性量表/逐步线性回归/人工神经网络Key words
stroke/subacute phase/machine learning/prediction/functional independence measure/stepwise linear regression/arti-ficial neural network分类
临床医学引用本文复制引用
曾形信,赖奕杉,殷敏..机器学习模型在亚急性期脑卒中患者康复后功能结局预测中的价值研究[J].中医康复,2025,2(1):12-17,6.基金项目
联勤保障部队第九四五医院院级管理基金(2024945YG-07) （2024945YG-07）
