多模组混合型结构方程建模与结构化的稀疏性,用于从异质健康数据中发现子组
Yu Ding1, Virend K Somers2, Bing Si3
1Thomas J. Watson College of Engineering and Applied Science, State University of New York at Binghamton, Binghamton NY.
概括
我们开发了一种新的机器学习模型 (M2-SEM),用于集群复杂的健康数据. 这种方法确定了针对性干预的不同子组,改善了人口健康结果.
科学领域:
- 医疗信息学 医疗信息学
- 机器学习 机器学习
- 生物统计学 生物统计学
背景情况:
- 越来越多的多模式,混合类型的健康数据 (生物库,电子健康记录,可穿戴设备) 需要先进的分析模型.
- 聚类旨在从异质数据中识别同质子组,用于有针对性的研究和干预.
- 聚类高维,多模,混合类型的数据对现有的统计和机器学习模型构成重大挑战.
研究的目的:
- 提出一种新的多模组混合型结构方程模型 (M2-SEM),具有结构化的稀疏性,用于在异质健康数据中精确地发现子组.
- 开发一个高效的算法 (GH-EMM) 用于估计混合数据类型 (连续和分类) 的模型.
主要方法:
- 开发了一种新的多模组混合型结构方程模型 (M2-SEM),结合了结构化的稀疏性.
- 创建了一个支持高斯-赫米特的预期-最大化-最小化 (GH-EMM) 算法,以在预期最大化 (EM) 框架内处理混合数据类型.
- 通过对基准模型进行广泛的模拟研究,验证了M2-SEM和GH-EMM.
主要成果:
- M2-SEM和GH-EMM显示了高维,多模式,混合类型的健康数据的有效集群.
- 该模型应用于心脏代谢 (CM) 风险因素,确定了低风险和高风险个体的不同子组.
- 该模型成功地整合了各种CM风险因素,包括营养,心理健康,体力活动和睡眠.
结论:
- 拟议的M2-SEM与GH-EMM提供了一种强大的方法,用于使用复杂的健康数据发现子组.
- 这种方法有助于早期识别有风险的个体,以进行有针对性的干预和促进人口健康.
- 利用多模式混合型健康数据对精准医学和公共卫生战略具有重大前景.
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