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在ALIVE中表征多病态:比较单个和组合聚类方法
Jacqueline E Rudolph1, Bryan Lau1, Becky L Genberg1
1Department of Epidemiology, Bloomberg School of Public Health, Johns Hopkins University, Baltimore, MD 21205, United States.
确定患有多种慢性疾病 (多发病) 的不同患者群体至关重要. 这项研究比较了聚类方法,以找到发现独特的多病态模式的最佳方法.
科学领域:
- 公共卫生 公共卫生
- 计算生物学 计算生物学
- 生物统计学 生物统计学
背景情况:
- 多病性,即存在两个或两个以上的慢性疾病,构成了重大的公共卫生挑战.
- 多病症的异质性使得研究变得复杂,个体之间病情的数量和组合各不相同.
- 无监督机器学习集群方法为识别不同的多病态现象类型提供了潜在的解决方案.
研究的目的:
- 评估和比较不同的聚类算法来识别多病态现象型.
- 评估集群组合方法在发现复杂健康模式中的有用性.
- 为选择适合多病症研究的集群方法提供指导.
主要方法:
- 应用三个个别的集群算法:围绕 medoids 分区,层次集群和概率集群.
- 利用聚类整体方法来整合来自多个算法的结果.
- 与静脉注射体验相关的艾滋病分析 (ALIVE) 队列研究数据.
- 基于质量,可解释性和预测能力的聚类结果的比较.
主要成果:
- 在ALIVE队列中展示了多个不同的多重疾病群.
- 个人和整体聚类方法的性能比较.
- 确定为特定研究目标选择最有效的集群策略的关键标准.
结论:
- 对比多个聚类算法和组合方法对于强大的多病态现象型识别至关重要.
- 选择集群方法应与已识别的患者子组的预期应用相一致.
- 这项研究为在复杂的健康结果研究中选择最佳集群技术提供了一个框架.
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