汤 洋

儿童哮喘的发生和病情波动并非由单一因素决定。遗传背景、免疫状态和环境暴露彼此交织，其中过敏原致敏在部分患儿中与症状反复密切相关。尘螨、花粉、霉菌、动物皮屑和食物相关过敏原的分布受地域、年龄及季节影响，其他地区的资料并不能完全代表常德儿童的实际情况。以本地病例为基础建立过敏原数据库，并同步记录年龄、季节、多重致敏、既往发作、肺功能和用药情况，更有利于判断哪些致敏模式与疾病控制不佳有关。人工智能的作用主要在于处理这类多维资料。特异性IgE、症状、肺功能及环境暴露等信息经整合后，可用于识别常见致敏组合和不同患儿的风险特征。本文结合区域过敏原谱、年龄变化、环境暴露及临床数据特点，对机器学习和大语言模型在儿童哮喘管理中的应用进行讨论。现阶段，这些工具仍属于辅助分析手段，不能取代规范检测及临床判断。

儿童哮喘；过敏原；区域特征；人工智能；机器学习；精准管理

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