基于公共卫生数据和深度学习,识别智能城市中人类暴露毒理学的生物指标
Peimao Gao1, Guowu Huang2, Lu Zhao1
1Chongqing General Hospital, Chongqing, China.
Frontiers in public health
|June 14, 2024
概括
本研究引入了一种深度学习方法,用于识别智能城市环境污染物暴露的生物指标. 该模型准确预测健康风险,有助于公共卫生保护和决策.
科学领域:
- 环境健康 环境健康
- 公共卫生 公共卫生
- 计算生物学 计算生物学
背景情况:
- 城市化增加了人口对环境污染物的暴露.
- 智慧城市的发展需要优先考虑公共卫生和风险管理.
- 识别毒理学生物指标对于评估暴露风险至关重要.
研究的目的:
- 提出一种基于深度学习的方法,用于识别智能城市中人类对毒理学生物指标的暴露.
- 准确评估和管理与环境污染物相关的公共卫生风险.
- 使用智慧城市数据建立环境和健康指标之间的相关模型.
主要方法:
- 通过智能城市传感器收集环境监测数据 (空气,水,土壤质量).
- 建立了一个数据库,将环境污染物类型/度与公共卫生数据联系起来.
- 利用卷积神经网络 (CNN) 来识别模式,相关性和生物指标.
主要成果:
- 在曝光风险评估中实现了93.45%的预测准确度.
- 在识别疾病 (呼吸道,心血管) 和污染物 (PM2.5,SO2) 之间的关联方面表现出高度的适配程度 (>0.90).
- 成功确定了与环境污染暴露相关的生物指标.
结论:
- 开发的深度学习模型为政府和卫生部门提供了有效的决策支持.
- 这项研究为保护城市环境中的公共卫生提供了新的策略.
- 该模型准确地识别了环境暴露的健康风险,增强了智慧城市的公共卫生倡议.
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