青年精神和行为障碍的社会环境决定因素:一种机器学习方法
Luke Wertis1, Margaret M Sugg1, Jennifer D Runkle2
1Department of Geography and Planning Appalachian State University Boone NC USA.
GeoHealth
|September 15, 2023
概括
极端的高温影响年轻人的心理健康,增加了心理和行为障碍 (MBD) 的急诊室访问. 机器学习确定了社会人口统计因素和环境条件作为关键预测因素,为公共卫生干预提供了信息.
科学领域:
- 环境健康 环境健康
- 青少年心理健康 青少年心理健康
- 机器学习应用 机器学习应用
背景情况:
- 极端的夏季环境条件与不良的精神和行为障碍 (MBD) 有关.
- 有限的研究存在于这些条件对年轻人群的影响.
- 了解青少年MBD的环境触发因素对于有针对性的干预至关重要.
研究的目的:
- 应用机器学习来识别青少年与MBD相关的急诊室 (ER) 访问的预测因素.
- 分析环境条件,社会人口学变量和青年MBD ER访问之间的关系.
- 在高温事件期间为MBD提供预测模型的信息.
主要方法:
- 利用了来自北卡罗来纳州城市的每日超过42,000个MBD相关的ER访问记录.
- 配对ER数据与日常环境和社会人口统计学变量.
- 应用了四种机器学习模型 (GLM,GAM,XGBoost,随机森林) 和一个分布式滞后非线性模型.
主要成果:
- 在总体分析中,社会人口统计学变量是MBD最强的预测因素.
- 在各个城市,温度 (max/min) 和正常化差异植被指数预测了MBD ER在24小时后的访问.
- 机器学习模型展示了与MBD相关的ER访问的预测性能.
结论:
- 社会人口统计学因素显著影响青年MBD,以及特定的环境触发因素,如温度.
- 机器学习有效地识别了影响青年MBD对环境压力因素反应的关键变量.
- 这些发现可以指导在极端天气期间为青年心理健康开发预测工具和干预措施.
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