对集群方法的概述,以及在心理健康研究中的应用指南
Caroline X Gao1, Dominic Dwyer2, Ye Zhu3
1Centre for Youth Mental Health, The University of Melbourne, Parkville, VIC, Australia; Orygen, Parkville, VIC, Australia; Department of Epidemiology and Preventative Medicine, School of Public Health and Preventive Medicine, Monash University, Melbourne, VIC, Australia.
本研究为心理健康研究选择和实施聚类算法提供了指导. 它涵盖了用于识别患者子组的各种方法,评估技术和报告标准.
科学领域:
- 心理健康研究 心理健康研究
- 计算统计学 计算统计学
- 数据科学数据科学数据科学
背景情况:
- 集群分析对于理解心理健康中的异质性至关重要.
- 现有的关于选择和应用集群方法的指导是有限的.
- 算法的进步需要更新最佳实践.
研究的目的:
- 为心理健康研究的聚类算法提供全面指导.
- 为了解决模型选择和报告的标准化框架的缺乏.
- 为了促进先进的集群技术的应用.
主要方法:
- 审查和引入与心理健康相关的主要集群算法.
- 讨论先进的方法:内核方法,深度学习,半监督集群和集群组合.
- 关于算法选择,数据预处理,评估和验证的指导.
主要成果:
- 聚类算法哲学,设计和权衡的详细概述.
- 介绍了解决集群分析中常见挑战的方法.
- 制定了对集群工作流程和报告要求的建议.
结论:
- 这篇论文为研究人员在心理健康中使用聚类提供了一个实用的框架.
- 标准化方法可以提高子组识别的可靠性和可解释性.
- 提供了实现各种算法的资源,包括R函数.
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