在6-14岁的中国儿童中,与过敏疾病相关的因素
Qiong Wang1,2, Min Yang3, Kening Chen4
1Graduate School, Beijing University of Chinese Medicine, Beijing, China.
BMC public health
|November 1, 2025
概括
机器学习确定了儿童过敏疾病的关键因素. 这项研究确定了在学龄儿童中喘,过敏性鼻炎和湿疹的最小风险预测因素.
科学领域:
- 儿科过敏和免疫学
- 计算流行病学计算流行病学
- 公共卫生 公共卫生
背景情况:
- 过敏性疾病在学龄儿童中很普遍.
- 识别导致因素对于早期干预和预防策略至关重要.
- 机器和深度学习为分析复杂的健康数据提供了新的方法.
研究的目的:
- 使用机器/深度学习算法,识别和优化与6-14岁儿童过敏疾病相关的因素.
- 开发喘,过敏性鼻炎和湿疹的预测模型.
- 为了发现这些条件的最小但重要的风险因子集.
主要方法:
- 在小学和中学采用集群抽样策略进行的横截面调查.
- 通过调查问卷从11,308名注册儿童收集数据.
- 使用Python (v3.7.6) 进行分析的12个机器/深度学习算法的实现.
主要成果:
- 喘,过敏性鼻炎和湿疹的患病率分别为6.31%,25.36%和21.38%.
- 高斯,伯努利和多项天真贝叶斯模型显示出喘,鼻炎和湿疹预测的卓越性能.
- 确定了最少的一组关键因素:喘 (6 个因素),鼻炎 (5 个因素) 和湿疹 (5 个因素),包括呼吸道感染,年龄,性别,家族病史,母亲教育和户外活动.
结论:
- 最少的一组因素有效地捕获了用于预测儿童过敏疾病的重要信息.
- 机器学习模型可以准确预测喘,过敏性鼻炎和湿疹的风险.
- 这些发现可以为儿童过敏症的有针对性的预防和管理策略提供信息.
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