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可解释机器学习用于帕金森病的认知障碍预测:使用SHAP分析的多中心验证研究

Ziyuan Wang¹, Junqiang Yan^1,2

¹Key Laboratory of Neuromolecular Biology, The First Affiliated Hospital, College of Clinical Medicine of Henan University of Science and Technology, Luoyang, China.

Frontiers in aging neuroscience

|November 27, 2025

概括

这项研究开发了一个可解释的机器学习模型,使用常规临床数据来检测帕金森病认知障碍 (PD-CI). 该模型实现了高准确性,识别了诸如年龄和炎症标志物等关键预测因素.

科学领域:

神经学神经学
人工智能的人工智能
生物标志物生物标志物

背景情况:

帕金森病 (PD) 通常导致认知障碍 (PD-CI),影响生活质量.
目前的PD-CI检测方法可能需要专门的资源或缺乏广泛适用性.
需要为PD-CI查提供可访问和可解释的工具.

研究的目的:

开发一个可解释的机器学习框架来检测PD-CI.
仅使用例行临床数据进行PD-CI预测.
确保该框架在不同人群中具有普遍性.

主要方法:

分析了1,279名来自帕金森病进展标记计划 (PPMI) 的参与者和197名来自独立队列的参与者.
根据蒙特利尔认知评估 (MoCA) 和统一帕金森病评分表第I部分 (UPDRS-I) 评分定义的PD-CI.
训练和优化四个机器学习模型,使用21个临床特征和交叉验证.

主要成果:

随机森林模型在发现队列中实现了最高的AUC (0.83).
外部验证证明了71.57%的准确性.
确定的主要预测因素包括年龄,中性粒细胞与淋巴细胞比率 (NLR) 和血清尿酸,表明炎症和氧化应激的作用.

结论:

开发的框架使用可访问的临床数据提供了与神经成像相比较的准确性.
它强调神经炎症和氧化应激是PD-CI的关键因素.
多中心验证支持其作为PD-CI查和监测的临床可行的工具.

关键词:

帕金森病是帕金森氏症的一种疾病.随机的森林随机的森林在XGBoost中使用.认知障碍是一种认知障碍.可以解释的机器学习.

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