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基于机器学习的非侵入性帕金森病诊断模型,使用临床血液生物标志物

Jiaqi Han¹, Mengge Sun², Ji Yang³

¹Department of Laboratory Medicine, Shanghai East Hospital, Tongji University School of Medicine, 1800 Yuntai Road, Shanghai, 200123, China. hanqiushu@126.com.

Neurological sciences : official journal of the Italian Neurological Society and of the Italian Society of Clinical Neurophysiology

|October 6, 2025

概括

使用血液生物标志物的机器学习模型可以有效地诊断帕金森病 (PD). 这项研究确定了超氧化物脱酶 (SOD) 作为PD诊断和严重程度的关键指标.

科学领域:

生物医学工程生物医学工程
计算生物学计算生物学
神经学神经学

背景情况:

帕金森病 (PD) 诊断缺乏可靠的非侵入性标志物,阻碍了早期检测和治疗.
与血液生物标志物集成的机器学习 (ML) 为早期PD诊断和监测提供了潜在的解决方案.

研究的目的:

开发和验证使用ML和常规血液生物标志物的PD非侵入性诊断模型.
确定与PD严重程度相关的关键生物标志物.

主要方法:

利用来自两个医疗中心的920名参与者 (428名PD,492名非PD) 的数据进行培训和验证.
采用LASSO和阶段回归来选择生物标志物,并构建/比较了五个ML模型 (LR,SVM,DT,NB,KNN).
使用Shapley值 (SHAP) 解释了最佳模型,并评估了与PD严重程度 (Hoehn-Yahr阶段) 的相关性.

主要成果:

支持矢量机 (SVM) 模型实现了高的外部验证性能 (AUC=0.916,回忆=0.949,F1分数=0.843).
在SHAP分析中,超氧化物脱酶 (SOD) 是最有影响力的预测因子,其次是性别和尿酸 (UA).
白蛋白 (ALB) 和SOD水平与PD严重程度有负相关性.

结论:

基于SVM的模型在区分PD与使用可访问的临床生物标志物的对照中表现出高效率.
这种方法显示了PD查,诊断和监测疾病进展的临床应用的巨大潜力.

关键词:

血液中的生物标志物诊断模型是一个诊断模型.机器学习机器学习氧化应激是一种氧化应激.帕金森病是帕金森病的一种.

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