This session aims to bring together researchers employing different methods from complexity science to address questions in healthcare. This includes approaches from dynamical systems, computational fluid dynamics, scientific machine learning, and uncertainty quantification. We will showcase how these approaches have been applied to different regimes in healthcare in the context of modelling and prediction, and bringing about innovative solutions.

Recent advancements in digital health have underscored the need and potential for interdisciplinary collaborations. For instance, scientific machine learning techniques have been recently used to identify appropriate missing terms in reduced order dynamical models using inputs from data. Similarly, statistical surrogate models have been proposed to approximate the behavior of complex system in computationally intensive tasks

Through this satellite session we expect to foster discussions among researchers, identifying pitfalls and potential solutions in the realm of complex systems in biology and health science.

This event will take place as part of the Conference on Complex Systems 2024. For more information about the venue please visit the conference website.