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机器学习用于使用临床指标预测酸性喘,作为诱导吐的替代方案

Lu Zhao¹, Gongqi Chen¹, Chunli Huang¹

¹Division of Respiratory and Critical Care Medicine, Department of Internal Medicine, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430030, China; Key Laboratory of Respiratory Diseases, National Health Commission of People's Republic of China, Wuhan, 430030, China.

Respiratory medicine

|November 21, 2025

概括

机器学习模型可以使用临床指标识别eosinophilic喘 (EA),为耗时的诱导唾液测试提供替代方案. 支持矢量机 (SVM) 模型在预测EA方面显示出有希望的结果.

科学领域:

肺部医学肺部医学
计算生物学计算生物学
过敏和免疫学过敏和免疫学

背景情况:

诱导唾液分析可以区分喘亚型,但需要大量的时间.
酸性喘 (EA) 的分类对于向治疗至关重要.
需要开发用于EA识别的非侵入性方法.

研究的目的:

开发和评估一种机器学习模型,以使用现有临床指标预测酸性喘 (EA).
评估各种AI算法的有效性,以识别EA.
建立一个计算效率高的替代品来诱导唾液分析.

主要方法:

利用了103名喘患者的数据,分为培训 (83) 和验证 (20) 队列.
五个人工智能算法,包括支持矢量机 (SVM),使用临床指标进行训练.
使用k-fold交叉验证和网格搜索优化了SVM的超参数和值.

主要成果:

SVM模型显示了EA的良好的区分,在培训队列中实现了0.93的曲线下的面积 (AUC) 和81.58%的准确性.
确定的关键预测因素包括呼出的氧化分数,血液中乙酸氨基和IgE.
在验证队列中,SVM模型实现了0.77的AUC,表明有利的预测能力.

结论:

使用临床指标的机器学习模型可以有效地预测eosinophilic喘 (EA).
这种方法为EA分类的诱导唾液分析提供了一个可行的,不那么侵入性的替代方案.
临床指标与人工智能相结合,显示了常规EA诊断的潜力.

关键词:

临床指标临床指标埃索诺菲尔性喘是什么意思诱导的吐引起的吐.机器学习是机器学习.预测预测预测

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