Kimpton Louise M, Paun L Mihaela, Colebank Mitchel J, Volodina Victoria
Department of Mathematics and Statistics, University of Exeter, Exeter, UK.
School of Mathematics and Statistics, University of Glasgow, Glasgow, UK.
Philos Trans A Math Phys Eng Sci. 2025 Mar 13;383(2292):20240232. doi: 10.1098/rsta.2024.0232.
Uncertainty quantification (UQ) is an essential aspect of computational modelling and statistical prediction. Multiple applications, including geophysics, climate science and aerospace engineering, incorporate UQ in the development and translation of new technologies. In contrast, the application of UQ to biological and healthcare models is understudied and suffers from several critical knowledge gaps. In an era of personalized medicine, patient-specific modelling, and , a lack of UQ understanding and appropriate implementation of UQ methodology limits the success of modelling and simulation in a clinical setting. The main contribution of our review article is to emphasize the importance and current deficiencies of UQ in the development of computational frameworks for healthcare and biological systems. As the introduction to the special issue on this topic, we provide an overview of UQ methodologies, their applications in non-biological and biological systems and the current gaps and opportunities for UQ development, as later highlighted by authors publishing in the special issue.This article is part of the theme issue 'Uncertainty quantification for healthcare and biological systems (Part 1)'.
不确定性量化（UQ）是计算建模和统计预测的一个重要方面。包括地球物理学、气候科学和航空航天工程在内的多个应用领域，在新技术的开发和转化过程中都纳入了不确定性量化。相比之下，不确定性量化在生物和医疗模型中的应用研究不足，且存在一些关键的知识空白。在个性化医疗、患者特异性建模的时代，对不确定性量化的理解不足以及不确定性量化方法的不当应用，限制了临床环境中建模与模拟的成功。我们这篇综述文章的主要贡献在于强调不确定性量化在医疗保健和生物系统计算框架开发中的重要性及当前存在的不足。作为关于该主题特刊的引言，我们概述了不确定性量化方法、它们在非生物和生物系统中的应用，以及不确定性量化发展当前存在的差距和机遇，后续在该特刊上发表文章的作者将对此进行重点阐述。本文是主题为“医疗保健和生物系统的不确定性量化（第1部分）”特刊的一部分。
