Enabling Longitudinal Exploratory Analysis of Clinical COVID Data

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...

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Bibliographic Details
Published in:2021 IEEE Workshop on Visual Analytics in Healthcare (VAHC) pp. 19 - 24
Main Authors: Borland, David, Brain, Irena, Fecho, Karamarie, Pfaff, Emily, Xu, Hao, Champion, James, Bizon, Chris, Gotz, David
Format: Conference Proceeding Journal Article
Language:English
Published: IEEE 01.01.2021
Cornell University
Subjects:
Collaboration
Conferences
COVID-19
Data visualization
Human-centered computing [Visualization]: Visualization application domains-Visual analytics
Human-centered computing [Visualization]: Visualization systems and tools
human-computer interaction
medical informatics
Medical services
Pandemics
temporal event sequence visualization
Visual analytics
ISSN:2331-8422, 2331-8422
Online Access:Get full text
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Summary: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.
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ISSN:2331-8422
2331-8422
DOI:10.1109/VAHC53616.2021.00008