一个过量预测仪表板,用于本地减少危害的应对措施
Maxwell Krieger1, Jesse Yedinak1, Ellen Duong2
1Brown University School of Public Health, Providence, RI, USA.
Health promotion practice
|May 6, 2025
概括
罗德岛州 罗德岛州 罗德岛州
科学领域:
- 公共卫生 公共卫生
- 数据科学数据科学数据科学
- 流行病学 流行病学
背景情况:
- 美国面临严重的过量危机,促使创新的公共卫生战略.
- 罗德岛州利用监测数据和多部门合作来预防过量服用.
研究的目的:
- 描述PROVIDENT仪表板的开发和试点工作.
- 评估社区组织 (CBO) 如何利用 ML 预测来减少危害.
主要方法:
- 在PROVIDENT项目中,机器学习 (ML) 用于社区层面的过量风险预测.
- 一个交互式在线映射仪表板与CBO共同开发.
- 测量了仪表板的使用,决策和培训参与度.
主要成果:
- 中央公关机构参与了PROVIDENT仪表板,以告知减少危害的服务分配.
- 仪表板促进了社区层面的数据驱动决策.
- 监控数据和ML洞察力指导了外展工作.
结论:
- 提供者仪表板有效地整合了ML和监测数据,以预防过量服用.
- 与CBO合作开发可以提高工具的实用性和采用.
- 这种方法为数据驱动的公共卫生干预提供了一个可扩展的模型.
关键词:
地理信息系统 (GIS) 是一个社区组织社区组织社区组织.数据仪表板数据仪表板数据可视化数据可视化预测 预测 预测 预测机器学习是机器学习.邻里社区 邻里社区预防过量服用 预防过量服用预测分析 预测分析更多相关视频
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