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管道优化机器学习用于慢性疲劳综合征诊断:使用血液生化和代谢数据的轻量级可解释模型

Junrong Li¹, Hanyu Cao², Zirun Zhu²

¹Centre for Artificial Intelligence Driven Drug Discovery, Faculty of Applied Sciences, Macao Polytechnic University, Macau 999078, Macao.

Computational biology and chemistry

|March 6, 2026

概括

一个新的诊断模型使用客观生物标志物和机器学习准确识别慢性疲劳综合征 (CFS). 这种可解释的工具有助于早期诊断和个性化治疗CFS患者.

科学领域:

生物化学生物化学
计算生物学计算生物学
医学诊断医学诊断医学诊断

背景情况:

慢性疲劳综合征 (CFS) 是一种复杂的多系统性疾病,其特点是持续的疲劳和功能障碍.
慢性疲劳症的低诊断源于症状异质性和缺乏客观生物标志物.
分析管道的系统优化对于开发轻量级,可解释的诊断模型至关重要.

研究的目的:

开发和验证一个全面的管道优化框架,用于CFS诊断.
确定用于CFS检测的关键客观生物标志物.
构建一个轻量级,可解释的机器学习模型用于CFS诊断.

主要方法:

利用英国生物库的1137例CFS病例和66838例对照病例的代谢和血液生物化学数据,包括健康个体和患有CFS重叠疾病的人.
采用了分层启动抽样,并比较了各种归算,特征选择和机器学习/深度学习模型.
包含孟德尔随机化 (MR) 和SHAP分析用于因果推断和特征贡献量化,以及决策曲线分析 (DCA) 用于临床实用性.

主要成果:

一个优化的管道产生了使用10个生物标志物和3个共变量的轻量化模型,实现了高诊断性能 (精度=0.939,ROC-AUC=0.979,MCC=0.878).
该模型有效地将CFS与健康对照和重叠条件区分开来,通过DCA证明了大量的临床实用性.
对六种生物标志物的MR分析确定了因果关系,而SHAP分析显示,高葡萄糖和白蛋白水平会加剧CFS症状.

结论:

系统的管道优化导致使用客观生物标志物的轻量级,高度可解释的CFS诊断模型.
开发的模型为早期CFS诊断和个性化治疗管理提供了强大的,具有成本效益的基础.
该方法通过ClinMetML框架实施,确保可复制性并促进临床应用.

关键词:

生物标志物识别识别方法慢性疲劳综合征慢性疲劳综合征深度学习 (Deep Learning) 是一种深度学习.可解释的人工智能机器学习机器学习风险预测模型模型

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