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Uncertainty is an ever-present phenomenon in healthcare that affects all stakeholders and can be seen as one of the main triggers for confusion, anxiety and challenge in decision making. This thesis presents a synthesis of themes from the works published in the period between 2018-2022 in collaboration with colleagues from Queen's University Belfast (UK) and Ontario Tech University (Canada) with the goal to present novel methodologies resulting from a fusion of statistical and AI techniques, the purpose of which is in aiding healthcare stakeholders in their decision making by minimising the negative effect of uncertainty. There are 7 publications in total that are forming the body of work of this thesis, of which 4 are journal articles, 2 are book chapters and 1 is in conference proceedings. Rather than present the papers in chronological order, they are included in the thesis where they are naturally best placed to suit the flow of the narrative. The main body of work, provides the detailed contributions that the publications have made to the statistics, computer science, health informatics and public health domains. In particular, the contributions are presented across the following three themes: (1) novel metrics for assessing the accuracy of streaming clinical decision support systems, (2) their deployment and accelerated benchmark testing, and (3) the development of a public health reporting model for mitigating the spread of infectious diseases in the population. The thesis concludes with a critical account of the significance of the published works, that is, the impact that these have made in the academic, industrial and public domains.

This portfolio of compositions uses novel biometrics from gestural interfaces called Myo armbands to compose five pieces of electroacoustic music within a continuum of spatial environments, ranging from the real to the virtual. The portfolio achieves this by processing biometric data from the Myo armbands within developed software (using Max 8), applying machine learning to such data (via Wekinator) and mapping predictive outputs to audiovisual materials across the spatial continuum. The results of this approach show how unique compositional affordances can be created when using biometrics within different spatial environments and how implementing machine learning methods can help to create such opportunities. The outcomes are presented via five composed portfolio works, three published papers within the scientific community, and numerous music performances navigating the overlapping areas of music composition and computer science. By using novel biometrics to compose music within a continuum of space, this interdisciplinary research topic contributes original knowledge to both the fields of music composition and computer science (human-computer interaction).