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Published on: September 16, 2015
Decoding uncertainty for clinical decision-making
Krasimira Tsaneva-Atanasova1, Giulia Pederzanil2, Marianna Laviola3
1Department of Mathematics and Living Systems Institute, University of Exeter, Exeter, UK.
Abstract:
In this opinion piece, we examine the pivotal role that uncertainty quantification (UQ) plays in informing clinical decision-making processes. We explore challenges associated with healthcare data and the potential barriers to the widespread adoption of UQ methodologies. In doing so, we highlight how these techniques can improve the precision and reliability of medical evaluations. We delve into the crucial role of understanding and managing the uncertainties present in clinical data (such as measurement error), diagnostic tools and treatment outcomes. We discuss how such uncertainties can impact decision-making in healthcare and emphasize the importance of systematically analysing them. Our goal is to demonstrate how effectively addressing and decoding uncertainties can significantly enhance the accuracy and robustness of clinical decisions, ultimately leading to better patient outcomes and more informed healthcare practices.This article is part of the theme issue 'Uncertainty quantification for healthcare and biological systems (Part 1)'.
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