Challenges and opportunities in uncertainty quantification for healthcare and biological systems

Abstract

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 digital twins, 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)'.

Keywords: biology and healthcare; clinical decision support; digital twins; mechanistic models; uncertainty quantification.

Figure 1.

UQ pipeline. At the center of the UQ pipeline is the computer model $f$ that provides a virtual representation of the physical system. Each coloured box corresponds to a distinct branch of UQ methods described in §3. The flowchart demonstrates the iterative nature of UQ, where input parameters ${\displaystyle \mathbf{\text{x}}}$ drive simulations that are continuously refined and validated, linking model outputs to real-world observations (${\displaystyle \mathbf{\text{z}}}$) from the physical system.

Figure 2.

Overarching flow of expertise for UQ. Each subdomain has its own specific skill sets which, when applied together in an interdisciplinary setting, can be used to better explore uncertainty in biological and healthcare problems.