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Authors: Federico Cabitza ; Andrea Campagner and Enrico Conte

Affiliation: Universita degli Studi di Milano-Bicocca, Viale Sarca 336, 20126, Milano, Italy

Keyword(s): Data Visualization, Medical Test Results, COVID-19, Uncertainty Communication.

Abstract: As most countries in the world still struggle to contain the COVID-19 breakout, Data Visualization tools have become increasingly important to support decision-making under uncertain conditions. One of the challenges posed by the pandemic is the early diagnosis of COVID-19: To this end, machine learning models capable of detecting COVID-19 on the basis of hematological values have been developed and validated. This study aims to evaluate the potential of two Data Visualizations to effectively present the output of a COVID-19 diagnostic model to render it online. Specifically, we investigated whether any visualization is better than the other in communicating a COVID-19 test results in an effective and clear manner, both with respect to positivity and to the reliability of the test itself. The findings suggest that designing a visual tool for the general public in this application domain can be extremely challenging for the need to render a wide array of outcomes that can be affected by varying levels of uncertainty. (More)

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Paper citation in several formats:
Cabitza, F.; Campagner, A. and Conte, E. (2022). Comparative Assessment of Two Data Visualizations to Communicate Medical Test Results Online. In Proceedings of the 17th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2022) - IVAPP; ISBN 978-989-758-555-5; ISSN 2184-4321, SciTePress, pages 195-202. DOI: 10.5220/0010968800003124

@conference{ivapp22,
author={Federico Cabitza. and Andrea Campagner. and Enrico Conte.},
title={Comparative Assessment of Two Data Visualizations to Communicate Medical Test Results Online},
booktitle={Proceedings of the 17th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2022) - IVAPP},
year={2022},
pages={195-202},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0010968800003124},
isbn={978-989-758-555-5},
issn={2184-4321},
}

TY - CONF

JO - Proceedings of the 17th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2022) - IVAPP
TI - Comparative Assessment of Two Data Visualizations to Communicate Medical Test Results Online
SN - 978-989-758-555-5
IS - 2184-4321
AU - Cabitza, F.
AU - Campagner, A.
AU - Conte, E.
PY - 2022
SP - 195
EP - 202
DO - 10.5220/0010968800003124
PB - SciTePress

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