血液中依赖于机器学习的血液中依赖于血液中依赖于机器学习的慢性鼻炎分类所揭示的临床关键特征
Masaaki Ishikawa1, Zhiqian Jiang2, Canh Hao Nguyen2
1Department of Otolaryngology, Head and Neck Surgery, Hyogo Prefectural Amagasaki General Medical Center, Amagasaki, Hyogo Prefecture, Japan.
International forum of allergy & rhinology
|December 5, 2025
概括
机器学习确定了血基细胞计数作为慢性鼻炎 (CRS) 的关键因素. 这种分类有助于识别严重的eosinophilic CRS,即使没有鼻,指导耳鼻喉科医生.
科学领域:
- 医学研究 医学研究
- 数据科学在医学中的数据科学
- 耳鼻喉科 耳鼻喉科 耳鼻喉科
背景情况:
- 慢性鼻炎 (CRS) 的病理生理学可以通过使用机器学习 (ML) 基于血液中异性蛋白的疾病分类来阐明.
- 在CRS患者中调查血液中异敏素的存在和严重程度提供了新的见解.
研究的目的:
- 使用ML探索CRS与血液中氨酸友爱症的关键特征.
- 根据绝对乙氨基酸计数 (AEC) 划分500/μL (设置-1) 和500/1500/μL (设置-2) 的截止值进行CRS分类.
主要方法:
- 探索性数据分析和四个ML模型用于在两个设置中进行分类.
- 莎普利添加式解释 (SHAP) 确定了表现最好的模型的关键分类特征.
- 单变量分析包括399名CRS患者.
主要成果:
- 机器学习模型,特别是随机森林 (设置-1) 和极端梯度增强 (设置-2),证明了有效性.
- 血基细胞计数成为CRS分类的首要预测因素.
- 在设置-2中,高C反应蛋白,高血基细胞计数,慢性乙性肺炎,低CT得分和低NP得分预测AEC≥1500/μL.
结论:
- ML分类突出了基细胞在CRS内外围血液氨的作用.
- 高AECCRS的特殊特征建议考虑具有独特并发症的eosinophilicCRS,即使没有鼻或轻微CT发现.
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