潜在类分析和机器学习用于在疑似神经病患者中预测和区分临床亚型
Sirui Wu1, Yike Huang1, Lan Luo1
1Department of Laboratory Medicine, West China Hospital of Sichuan University, Chengdu, China.
Frontiers in cellular and infection microbiology
|December 11, 2025
概括
这项研究使用潜阶级分析确定了三种神经菌亚型. 一个机器学习模型准确地区分这些亚型,帮助临床诊断和治疗神经病.
科学领域:
- 传染性疾病 传染性疾病
- 神经学 神经学
- 医疗信息学 医疗信息学
背景情况:
- 由于症状多样化和缺乏黄金标准,诊断神经是具有挑战性的.
- 异质的临床表现和治疗反应使神经病管理复杂化.
研究的目的:
- 为了确定疑似神经病患者的临床同质亚型.
- 开发一个机器学习模型用于神经子的亚型,以帮助临床决策.
主要方法:
- 隐性类分析 (LCA) 用于从451例疑似神经病例中识别患者亚型.
- 拉索回归和博鲁塔算法选择了模型开发的关键预测变量.
- 训练了6个机器学习算法,选择了XGBoost以获得最佳性能.
主要成果:
- 在LCA中发现了三种亚型:典型的神经,非典型的神经和非神经.
- 在区分亚型方面,XGBoost模型实现了高精度 (87.3%的内部验证,91.5%的外部验证).
- 关键预测因素包括CSF核细胞,CSFTT和IgG指数.
结论:
- 确定了三种临床相关的神经病亚型.
- XGBoost模型有效地区分神经病亚型,支持及时诊断和治疗.
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