潜在类模型
范畴变量
星团(航天器)
梅德林
班级(哲学)
数据科学
心理学
医学
计算生物学
计算机科学
生物
人工智能
机器学习
生物化学
程序设计语言
标识
DOI:10.1177/0193945916679812
摘要
The purpose of this article is to provide an overview of latent class analysis (LCA) and examples from symptom cluster research that includes biomarkers and genetics. A review of LCA with genetics and biomarkers was conducted using Medline, Embase, PubMed, and Google Scholar. LCA is a robust latent variable model used to cluster categorical data and allows for the determination of empirically determined symptom clusters. Researchers should consider using LCA to link empirically determined symptom clusters to biomarkers and genetics to better understand the underlying etiology of symptom clusters. The full potential of LCA in symptom cluster research has not yet been realized because it has been used in limited populations, and researchers have explored limited biologic pathways.
科研通智能强力驱动
Strongly Powered by AbleSci AI