Enabling Longitudinal Exploratory Analysis of Clinical COVID Data

TL;DR

This paper explores the application of visual analytics to longitudinal COVID-19 clinical data, detailing data processing, initial insights, and lessons learned to support exploratory analysis and hypothesis generation.

Contribution

It demonstrates how existing event sequence visual analytics can be adapted for longitudinal clinical COVID data analysis, highlighting necessary data transformations and initial findings.

Findings

Identified key features of COVID-19 clinical data

Gained initial insights into disease progression patterns

Collected qualitative feedback for future improvements

Abstract

As the COVID-19 pandemic continues to impact the world, data is being gathered and analyzed to better understand the disease. Recognizing the potential for visual analytics technologies to support exploratory analysis and hypothesis generation from longitudinal clinical data, a team of collaborators worked to apply existing event sequence visual analytics technologies to a longitudinal clinical data from a cohort of 998 patients with high rates of COVID-19 infection. This paper describes the initial steps toward this goal, including: (1) the data transformation and processing work required to prepare the data for visual analysis, (2) initial findings and observations, and (3) qualitative feedback and lessons learned which highlight key features as well as limitations to address in future work.