  With the advances of data-driven machine learning research, a wide variety of
prediction problems have been tackled. It has become critical to explore how
machine learning and specifically deep learning methods can be exploited to
analyse healthcare data. A major limitation of existing methods has been the
focus on grid-like data; however, the structure of physiological recordings are
often irregular and unordered which makes it difficult to conceptualise them as
a matrix. As such, graph neural networks have attracted significant attention
by exploiting implicit information that resides in a biological system, with
interactive nodes connected by edges whose weights can be either temporal
associations or anatomical junctions. In this survey, we thoroughly review the
different types of graph architectures and their applications in healthcare. We
provide an overview of these methods in a systematic manner, organized by their
domain of application including functional connectivity, anatomical structure
and electrical-based analysis. We also outline the limitations of existing
techniques and discuss potential directions for future research.
