同时发生的社会决定因素的基于人口的聚类:无监督机器学习的应用
Ingrid Giesinger1, Emmalin Buajitti1, Arjumand Siddiqi2
1Dalla Lana School of Public Health, University of Toronto, Dalla Lana School of Public Health, Health Sciences Building, 155 College Street, 6th Floor, Toronto, Ontario, Canada, M5T 3M7.
机器学习根据健康决定因素识别出不同的社会群体. 这种数据驱动的方法有助于了解针对性卫生干预和政策制定的复杂社会暴露.
科学领域:
- 公共卫生 公共卫生
- 数据科学数据科学数据科学
- 社会学 社会学 社会学
背景情况:
- 健康的社会决定因素 (SDOH) 是复杂的和多因素的.
- 现有的措施往往难以捕捉同时发生的SDOH的相互作用.
- 需要采用数据驱动的方法来根据SDOH对人口子组进行分类.
研究的目的:
- 开发基于集群的衡量健康的共同发生的社会决定因素.
- 将无监督机器学习应用于基于人口的队列,用于SDOH分析.
- 创建一个数据驱动的方法来组织复杂的社会暴露.
主要方法:
- 对加拿大社区健康调查数据 (2001-2012) 进行了无监督集群.
- 与加拿大人口普查和重要统计数据相关的数据.
- 使用内部指标,可视化,描述性分析和过早死亡率评估集群有效性.
主要成果:
- 最佳的集群解决方案产生了4和6个集群,揭示了不同的社会类型学.
- 六个集群解决方案提供了更大的细节性和解释性.
- 在各个集群中,过早死亡率差异很大,这证明了该方法能够捕捉社会风险的能力.
结论:
- 无监督机器学习有效地根据复杂的社会暴露来识别人口子组.
- 这种灵活的,数据驱动的方法有助于描述SDOH.
- 调查结果支持在股权监测,干预计划和政策制定方面的应用.
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