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引用次数: 0
摘要
许多现有的健康社会决定因素综合指数(如地区贫困指数)都因依赖于地理近似值和美国社区调查数据而受到限制。本研究以有关贫困指数的大量文献为基础,在美国国立卫生研究院(NIH)的 "我们所有人 "数据网络(All of Us Data Network)中,通过对在参与者层面收集的 SDOH 数据元素进行加权多重对应分析,构建了个人社会经济贫困指数(ISDI)。在本研究中,将尽可能评估 ISDI 与另一个地区近似指数之间的相关性,以及基于 ISDI 五分位数的分层抽样导致的人工智能模型性能变化。个人层面的贫困指数可能具有广泛的实用性,尤其是在集中式和分布式数据网络中的精准医疗方面。
Development and Validation of an Individual Socioeconomic Deprivation Index (ISDI) in the NIH's All of Us Data Network.
Many of the existing composite social determinant of health indices, such as Area Deprivation Index, are constrained by their reliance on geographic approximations and American Community Survey data. This study builds on the body of literature around deprivation indices to construct an individual socioeconomic deprivation index (ISDI) within the NIH's All of Us Data Network by using weighted multiple correspondence analysis on SDOH data elements collected at the participant level. In this study, the correlation between ISDI and another area-approximated index is assessed to the extent possible, along with the changes in an AI models performance due to stratified sampling based on ISDI quintiles. Individual level deprivation indices may have a wide range of utility particularly in the context of precision medicine in both centralized and distributed data networks.