Vague Visualizations to Reduce Quantification Bias in Shared Medical Decision Making

Michela Assale, Silvia Bordogna, Federico Cabitza

2020

Abstract

This paper aims to contribute to the research focusing on how to render properly uncertainty in decision making, especially in regard to classification (like in medical diagnosis) or risk prediction (like in medical prognosis). Information visualizations leverage perception to convey information on data in ways that make their interpretation easier. Unfortunately, many visualizations omit uncertainty or communicate it less than effectively. We devised a novel way, which we call vague visualization, to render uncertainty without converting it in any numerical or symbolic form, and tested the usability and task fitness of these alternative solutions in a user study that involved a panel of lay people (as proxies of potential patients). In so doing, we aimed to understand whether our solutions facilitate (or at least do not hinder) communication and understanding of probabilistic estimates in a medical context, and if one solution is more effective than the others. We observed that three different vague visualizations convey the right sense of risk with respect to chance (50%) of percentage shown, and inspire an interpretation of the magnitude of the percentages that replicates the typical response of decision making under uncertainty condition. We then claim that these methods are effective because they allow for data interpretations that are uncertain (vague), and yet correct and compatible with appropriate decisions and actions.

Paper Citation

in Harvard Style

Assale M., Bordogna S. and Cabitza F. (2020). Vague Visualizations to Reduce Quantification Bias in Shared Medical Decision Making. In Proceedings of the 15th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2020) - Volume 3: IVAPP; ISBN 978-989-758-402-2, SciTePress, pages 209-216. DOI: 10.5220/0008969802090216

in Bibtex Style

@conference{ivapp20,
author={Michela Assale and Silvia Bordogna and Federico Cabitza},
title={Vague Visualizations to Reduce Quantification Bias in Shared Medical Decision Making},
booktitle={Proceedings of the 15th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2020) - Volume 3: IVAPP},
year={2020},
pages={209-216},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0008969802090216},
isbn={978-989-758-402-2},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 15th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2020) - Volume 3: IVAPP
TI - Vague Visualizations to Reduce Quantification Bias in Shared Medical Decision Making
SN - 978-989-758-402-2
AU - Assale M.
AU - Bordogna S.
AU - Cabitza F.
PY - 2020
SP - 209
EP - 216
DO - 10.5220/0008969802090216
PB - SciTePress