人工智能平台预测儿童医院护理呼吸系统疾病使用临床,污染和气候因素
William Cabral-Miranda1, Cauê Beloni1, Felipe Lora2
1Instituto de Pesquisa e Ensino em Saúde Infantil (PENSI Institute) - José Luiz Setúbal Foundation, São Paulo, Brazil.
Journal of global health
|July 21, 2025
概括
人工智能通过分析临床和环境数据来预测儿科呼吸急诊. 人工智能平台有助于医院预测患者流动并有效地分配资源,特别是在高污染时期.
科学领域:
- 环境健康 环境健康
- 医疗保健中的人工智能
- 儿童呼吸道疾病 儿童呼吸道疾病
背景情况:
- 空气污染是儿童健康结果的重要风险因素.
- 医院可以利用人工智能 (AI) 和大数据来分析临床和外部数据源.
- 机器学习 (ML) 算法可以基于训练数据生成预测模型.
研究的目的:
- 开发和验证基于人工智能的平台,用于预测儿科急诊和呼吸系统疾病的住院.
- 整合临床,环境,污染和气候数据用于预测建模.
- 使用XGBoost ML算法来预测医院利用率.
主要方法:
- 应用了XGBoost算法来预测萨巴拉儿童医院对呼吸系统疾病 (ICD-10 J,不包括COVID-19) 的医院使用情况.
- 分析了2022年1月至12月在巴西圣保罗的24,366次紧急访问和2973次入院.
- 纳入了临床,污染 (PM10) 和气候 (相对湿度) 变量,以及季节性和性别数据.
主要成果:
- 住宅附近的颗粒物 (PM10) 度增加与更高的门诊访问有关.
- 较低的相对湿度,特别是在干燥的日子里,与增加的住院病例有关.
- 春季季节和男性性别也与更高的入学率有关.
结论:
- 开发了一个人工智能平台,以预测儿童呼吸系统疾病的每日急诊室和住院流量.
- 该平台将临床和环境数据集成到一个大数据框架中.
- 这种预测工具可以帮助医院规划和资源分配,特别是在环境脆弱的地区.
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