Challenges of Clustering Multimodal Clinical Data: Review of Applications in Asthma Subtyping.

ABSTRACT:

Background

In the current era of personalized medicine, there is increasing interest in understanding the heterogeneity in disease populations. Cluster analysis is a method commonly used to identify subtypes in heterogeneous disease populations. The clinical data used in such applications are typically multimodal, which can make the application of traditional cluster analysis methods challenging.

Objective

This study aimed to review the research literature on the application of clustering multimodal clinical data to identify asthma subtypes. We assessed common problems and shortcomings in the application of cluster analysis methods in determining asthma subtypes, such that they can be brought to the attention of the research community and avoided in future studies.

Method

SUBMITTER: Horne E 

PROVIDER: S-EPMC7290450 | biostudies-literature | 2020 May

REPOSITORIES: biostudies-literature