Personalized Digital Twins for Chronic Disease Management with Wearables and Explainable AI

These projects are open to students worldwide, but have no funding attached. Therefore, the successful applicant will be expected to fund tuition fees at the relevant level (home or international) and any applicable additional research costs. Please consider this before applying.

Chronic diseases such as diabetes and cardiovascular disease represent a growing global health burden, requiring continuous monitoring and individualized care strategies. Traditional approaches often rely on generalized treatment guidelines, which may overlook the unique physiological and lifestyle factors that shape each patient’s health trajectory. This project aims to address this gap by developing personalized digital twins—virtual patient models built from real-world data—that can simulate, predict, and optimize chronic disease management.

Leveraging continuous data streams from wearable sensors (e.g., heart rate monitors, glucose sensors, respiratory trackers), combined with electronic health records and environmental information, these digital twins will dynamically reflect the evolving health state of individual patients. By integrating explainable AI (XAI) methods, the system will not only predict health risks and treatment outcomes but also provide transparent, interpretable insights into which factors drive those predictions—such as lifestyle patterns, medication adherence, or physiological anomalies.

The research will focus on developing robust frameworks for federated learning, enabling collaborative model training across distributed datasets without compromising patient privacy. This ensures that digital twins are both scalable and ethically deployed, while capturing diverse patient populations and disease variations.

Through simulation and predictive modelling, the project will enable clinicians to test “what-if” scenarios—for example, adjusting medication, altering lifestyle routines, or introducing new therapies—before applying them in real life. Patients, in turn, will gain access to personalized, trustworthy insights about their condition, fostering greater engagement in self-care.

By combining wearables, explainable AI, and federated learning, this interdisciplinary research sits at the intersection of computer science, biomedical engineering, and healthcare. Its outcomes have the potential to transform chronic disease management, offering patient-specific, data-driven, and transparent decision support tools that advance the future of digital health.

Informal enquiries can be made by contacting Dr R Islam (riazul.islam@abdn.ac.uk).

Decisions will be based on academic merit. The successful applicant should have, or expect to obtain, a UK Honours Degree at 2.1 (or equivalent) in Computing Science.

We encourage applications from all backgrounds and communities, and are committed to having a diverse, inclusive team.

Application Procedure:

Formal applications can be completed online: https://www.abdn.ac.uk/pgap/login.php.

You should apply for Degree of Doctor of Philosophy in Computing Science to ensure your application is passed to the correct team for processing.

Please clearly note the name of the lead supervisor and project titleon the application form. If you do not include these details, it may not be considered for the project.

Your application must include: A personal statement, an up-to-date copy of your academic CV, and clear copies of your educational certificates and transcripts.

Please note: you do not need to provide a research proposal with this application.

If you require any additional assistance in submitting your application or have any queries about the application process, please don't hesitate to contact us at researchadmissions@abdn.ac.uk