Characterizing multimorbidity in ALIVE: comparing single and ensemble clustering methods.

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 mul

SUBMITTER: Rudolph JE 

PROVIDER: S-EPMC11299029 | biostudies-literature | 2024 Aug

REPOSITORIES: biostudies-literature