Research and publications
Science-driven innovation
Published papers
Despite the proliferation of AI technologies, their uptake in clinical settings has been lacking progress due to complexities of sociotechnical factors and intricacies of decision-making. Fairness and bias of predictive models, ethics and quality of training data, and corresponding compliance requirements become especially pressing while remaining fuzzy and implicit for various stakeholders who make the decisions. We propose a novel approach to AI-Empowered Clinical Decision Support System design based on Knowledge Graph representation.
Artificial intelligence (AI) has the potential to transform early diagnosis and subsequent treatment of neurological conditions, such as Parkinson’s disease. Early diagnosis means quicker access to vital care and support that could improve an individual’s quality of life and disease progression. But the applications of AI do not stop there – AI is also helping researchers to better understand the causes of Parkinson’s by assessing the changes in the brain at the initial stages of the disease. These early insights could be the key to developing new and better treatments.
Researching people with herpes simplex virus (HSV) is challenging because of poor data quality, low user engagement, and concerns around stigma and anonymity. This project aimed to improve data collection for a real-world HSV registry by identifying predictors of HSV infection. This machine learning algorithm can be used in a real-world evidence registry to collect relevant lifestyle data and identify individuals' levels of risk.
Comprehensive pharmacogenomic understanding requires both robust genomic and demographic data. Patient registries present an opportunity to collect large amounts of robust, patient-level data. This paper aims to understand the key factors that impact data collection quality for medical registries and suggest potential design features of an HSV medical registry to overcome current constraints and allow for this data to be used as a complement to genomic and clinical data to further the treatment of HSV.
This project aims to provide a tool for older people and their families and peers to improve their well-being and health during and after regulated social distancing. Making use of a pre-existing software framework for health behavior change, a proof of concept was developed, and a multistage app development and deployment for the solution was created. This case study lays the foundations for future app development to combat mental and societal issues arising from social distancing measures. This novel contribution will provide an evidence-based exemplar for future app development in the space of social isolation and loneliness.
In digital signal processing based long-haul coherent optical communication systems, an effect of equalization enhanced phase noise occurs due to the interplay between laser phase noise and electronic dispersion compensation module. It will significantly degrade the performance of uncompensated long-haul coherent optical fibre communication systems. Distortions from equalization enhanced phase noise are more severe for systems with larger transmission bandwidths, higher-order modulation formats and longer transmission distances. In this work, the performance and the modelling of long-haul optical fibre communication systems are discussed taking into account both equalization enhanced phase noise and fibre nonlinearities.
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Our research methods
Focus Groups, Interviews, and Co-Creation
Co-creation allows for a more holistic approach to digital system development, taking into account the perspectives and experiences of a variety of stakeholders, such as users, experts in the field, and members of the communities that will be impacted by the technology. By involving these stakeholders, ethical considerations can be identified and addressed early on in the design process, ultimately resulting in more responsible and inclusive AI systems.
Moreover, co-creation fosters transparency and accountability, enabling stakeholders to understand the technology and how it works. This helps build trust in the AI systems and ensures that they are developed with the best interests of all stakeholders in mind.
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