聚类分析
可解释性
中胚层
计算机科学
层次聚类
单连锁聚类
机器学习
人工智能
共识聚类
数据挖掘
多发病率
相关聚类
模糊聚类
概率逻辑
星团(航天器)
约束聚类
CURE数据聚类算法
医学
共病
病理
程序设计语言
作者
Jacqueline E. Rudolph,Bryan Lau,Becky L. Genberg,Jing Sun,Gregory D. Kirk,Shruti H. Mehta
摘要
Abstract Multimorbidity, defined as having 2 or more chronic conditions, is a growing public health concern, but research in this area is complicated by the fact that multimorbidity is a highly heterogenous outcome. Individuals in a sample may have a differing number and varied combinations of conditions. Clustering methods, such as unsupervised machine learning algorithms, may allow us to tease out the unique multimorbidity phenotypes. However, many clustering methods exist, and choosing which to use is challenging because we do not know the true underlying clusters. Here, we demonstrate the use of 3 individual algorithms (partition around medoids, hierarchical clustering, and probabilistic clustering) and a clustering ensemble approach (which pools different clustering approaches) to identify multimorbidity clusters in the AIDS Linked to the Intravenous Experience cohort study. We show how the clusters can be compared based on cluster quality, interpretability, and predictive ability. In practice, it is critical to compare the clustering results from multiple algorithms and to choose the approach that performs best in the domain(s) that aligns with plans to use the clusters in future analyses.
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