人工智能生物标志物检测高风险儿童喘亚组的呼吸道感染和恶化
Young J Juhn1, Chung-Il Wi1, Euijung Ryu2
1Department of Pediatric and Adolescent Medicine, Mayo Clinic, Rochester, Minn; Precision Population Science Lab, Mayo Clinic, Rochester, Minn.
The Journal of allergy and clinical immunology
|August 21, 2025
概括
自然语言处理 (NLP) 数字生物标志物可以识别急性呼吸道感染 (ARI) 的高风险儿童喘亚群. 在NLP-PAC+/NLP-API+小组中,肺炎,流感和喘恶化的风险增加.
科学领域:
- 儿童肺病学
- 数字健康
- 生物标志物发现
背景情况:
- 儿童喘增加了急性呼吸道感染的风险.
- 使用数字生物标志物识别高风险喘亚组至关重要.
- 对于儿童早期喘患者的ARI风险的了解有限.
研究的目的:
- 评估自然语言处理 (NLP) 驱动的数字生物标志物是否可以识别儿童喘的高危子组.
- 调查这种高风险表型的早期出现.
主要方法:
- 将验证的NLP算法 (NLP-PAC和NLP-API) 应用于大量出生队列的电子健康记录.
- 根据NLP-PAC和NLP-API标准将受试者分为四个子组.
- 在3岁和生命的前3年评估了特定ARI和喘恶化风险.
主要成果:
- 在NLP-PAC+/NLP-API+小组中,肺炎,甲型/乙型流感和喘恶化的风险最高.
- 这种肺炎,流感A/ B和呼吸道同胞病毒感染的风险增加在生命早期 (3年).
- 没有发现子组之间其他ARI的显著差异.
结论:
- NLP-PAC+/NLP-API+表型是用于识别高风险儿童喘的新型数字生物标志物.
- 这一小组特别容易感染肺炎,甲型/乙型流感和喘恶化.
- 这些疾病的高风险表型可能在儿童早期表现出来.
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