美国集群:利用机器学习了解美国的健康和医疗差距

Diana M Bowser, Kaili Maurico, Brielle A Ruscitti, William H Crown
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摘要

美国的健康和医疗服务存在严重的不平等。虽然机器学习(ML)越来越多地用于临床环境,为医疗服务决策提供信息并预测医疗服务的使用情况,但使用 ML 作为研究工具来了解美国的医疗差距以及这些差距如何与健康结果、医疗服务的获取和医疗系统的组织相关联,却并不常见。我们利用了医疗保健研究与质量机构(AHRQ)在其健康社会决定因素数据库(SDOH)中汇总的 24 个不同数据库中的 650 多个变量。我们使用 K-均值--一种非分层 ML 聚类方法--对县级数据进行聚类。主因子分析为每个 SDOH 领域和两个医疗保健领域(医疗保健基础设施和医疗保健获取)创建了县级指数值。逻辑回归分类用于确定聚类分类的主要驱动因素。在美国,最有效的聚类分类由 3 个不同的聚类组成;预期寿命最高的聚类只占县的 10%。最有效的 ML 聚类并不能确定医疗差距最大的聚类。使用县级数据进行的 ML 聚类显示,医疗保健基础设施和获取途径是聚类构成的主要驱动因素。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
American Clusters: Using Machine Learning to Understand Health and Health Care Disparities in the United States
Health and health care access in the U.S. is plagued by high inequality. While machine learning (ML) is increasingly used in clinical settings to inform health care delivery decisions and predict health care utilization, using ML as a research tool to understand health care disparities in the U.S. and how these are connected to health outcomes, access to health care, and health system organization is less common. We utilized over 650 variables from 24 different databases aggregated by the Agency for Healthcare Research and Quality (AHRQ) in their Social Determinant of Health Database (SDOH). We used k-means–a non-hierarchical ML clustering method–to cluster county level data. Principal factor analysis created county level index values for each SDOH domain and two health care domains–health care infrastructure and health care access. Logistic regression classification was used to identify the primary drivers of cluster classification. The most efficient cluster classification consists of 3 distinct clusters in the U.S.; the cluster having the highest life expectancy comprised only 10% of counties. The most efficient ML clusters do not identify the clusters with the widest health care disparities. ML clustering, using county level data, shows that health care infrastructure and access are the primary drivers of cluster composition.
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