Visual Subpopulation Discovery and Validation in Cohort Study Data

TL;DR

This paper introduces S-ADVIsED, an interactive visual analytics framework that helps epidemiologists discover, explore, and validate meaningful subpopulations in large, heterogeneous cohort study data using subspace clustering and visualization techniques.

Contribution

The paper presents a novel visual analytics tool that integrates subspace clustering with validation features, enabling epidemiologists to explore and verify subpopulation findings effectively.

Findings

Identified a subpopulation with high liver fat that deviates in multiple health variables.

Demonstrated the tool's capability to validate subpopulations across different cohorts.

Provided insights into health risk factors through visual exploration.

Abstract

Epidemiology aims at identifying subpopulations of cohort participants that share common characteristics (e.g. alcohol consumption) to explain risk factors of diseases in cohort study data. These data contain information about the participants' health status gathered from questionnaires, medical examinations, and image acquisition. Due to the growing volume and heterogeneity of epidemiological data, the discovery of meaningful subpopulations is challenging. Subspace clustering can be leveraged to find subpopulations in large and heterogeneous cohort study datasets. In our collaboration with epidemiologists, we realized their need for a tool to validate discovered subpopulations. For this purpose, identified subpopulations should be searched for independent cohorts to check whether the findings apply there as well. In this paper we describe our interactive Visual Analytics framework S-ADVIsED for SubpopulAtion Discovery and Validation In Epidemiological Data. S-ADVIsED enables epidemiologists to explore and validate findings derived from subspace clustering. We provide a coordinated multiple view system, which includes a summary view of all subpopulations, detail views, and statistical information. Users can assess the quality of subspace clusters by considering different criteria via visualization. Furthermore, intervals for variables involved in a subspace cluster can be adjusted. This extension was suggested by epidemiologists. We investigated the replication of a selected subpopulation with multiple variables in another population by considering different measurements. As a specific result, we observed that study participants exhibiting high liver fat accumulation deviate strongly from other subpopulations and from the total study population with respect to age, body mass index, thyroid volume and thyroid-stimulating hormone.