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通过机器学习揭示了通过机器学习预测慢性鼻炎的并发性慢性异性肺炎的关键特征

Masaaki Ishikawa¹, Zhiqian Jiang², Canh Hao Nguyen²

¹Department of Otolaryngology, Head and Neck Surgery, Hyogo Prefectural Amagasaki General Medical Center, Amagasaki, Japan.

International forum of allergy & rhinology

|May 30, 2025

概括

机器学习在患有慢性鼻炎 (CRS) 的患者中确定了慢性乙性肺炎 (CEP) 的关键指标. 血液中高血红蛋白细胞计数,白细胞计数和C反应蛋白水平是关键的诊断标志物.

科学领域:

肺部病理学肺部病理学
耳鼻喉科耳鼻喉科耳鼻喉科
数据科学数据科学数据科学

背景情况:

慢性乙性肺炎 (CEP) 可以与慢性鼻炎 (CRS) 一起发生.
在CRS患者中并发性CEP的关键诊断特征尚不清楚.

研究的目的:

通过机器学习研究CRS患者共发性CEP的关键特征.
在CRS中识别CEP时平衡预测性能和可解释性.

主要方法:

采用机器学习模型,包括极端梯度提升 (XGBoost),随机森林 (RF) 和决策树 (DT).
利用夏普利增量解释 (SHAP) 来提高预测模型的可解释性.
分析了372个慢性鼻炎样本,其中21个也被诊断为CEP.

主要成果:

XGBoost和RF模型显示出优异的预测性能 (AUC).
决策树分析确定了血中乙氨基酸细胞计数 (≥1446/μL),白细胞计数 (≥9.25 × 103 /μL) 和C反应蛋白 (≥0.335 mg/dL) 作为主要预测因素.
SHAP分析证实了这些发现,突出显示了高氨基,CRP和WBC作为关键指标.

结论:

决策树和SHAP始终将血中乙氨基细胞计数,白细胞计数和CRP确定为诊断CRS患者共发性CEP的前三大特征.
符合决策树标准的CRS患者可能与CEP同时患有Eosinophilic慢性鼻炎 (ECRS).
耳鼻喉科医生应考虑对疑似并发性ECRS和CEP患者进行鼻腔活检和胸部成像.

关键词:

慢性埃索诺菲尔性肺炎是什么慢性鼻鼻炎是什么埃索诺菲利亚 (eosinophilia) 是一种机器学习是机器学习.

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