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开发一种可解释的机器学习模型来预测中风后焦虑:使用Shapley添加式解释和名ogram可视化的多中心研究

Mengke Lyu¹, Yanming Xie², Min Li^3,4

¹Department of Encephalopathy, The First Affiliated Hospital of Henan University of Chinese Medicine, Zhengzhou, Henan, China.

|January 12, 2026

概括

这项研究开发了一种可解释的机器学习模型,使用临床数据预测中风后焦虑症 (PSA) 风险. 该模型准确识别高风险患者,使个性化干预措施能够改善中风恢复结果.

科学领域:

神经学神经学
人工智能的人工智能
临床医学临床医学

背景情况:

脑卒中后焦虑症 (PSA) 对康复和生活质量产生负面影响.
现有的PSA预测方法缺乏特征选择和可解释性.
早期检测高风险PSA患者对于及时干预至关重要.

研究的目的:

开发一种可解释的机器学习模型,用于早期检测高风险PSA患者.
利用包括人口,临床,生化和心理社会因素在内的综合数据集.
为了实现个性化干预,以改善中风后的结果.

主要方法:

对238名中风患者进行了回顾性多中心研究.
使用单变量分析和LASSO回归的特征选择.
开发和评估七个机器学习模型,包括逻辑回归和XGBoost,并进行交叉验证.
应用SHAP (Shapley添加式解释) 对于特征重要性和名ogram开发.

主要成果:

后勤回归模型实现了AUC为0.981,准确度为0.917,灵敏度为0.867,特异性为0.952,F1得分为0.897.
确定的主要预测因素包括复发性中风,社会经济因素,生活方式,并发病 (高血压,糖尿病),生化标志物 (WBC,TC,LDL,FIB,APTT) 和临床得分 (NIHSS,Barthel指数).
为了临床决策,创建了一个包含SHAP排名前10名特征的名图.

结论:

开发的机器学习模型在预测PSA风险方面表现出高准确性和可解释性.
SHAP分析和名图可视化为临床医生提供了一个实用的工具.
这种方法促进了早期识别高风险PSA患者和个性化管理策略.

关键词:

脑卒中后的焦虑症脑卒中后的焦虑症这就是 SHAP SHAP 的意思.可以解释的人工智能AI 机器学习是机器学习.这个名字是名ogramogram.风险预测风险预测中风康复中风康复中风康复

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