DARTH group mission
Ever-increasing healthcare provision demands strain national health systems globally, which struggle to meet patients' needs. There is a growing unmet clinical need to develop accurate, robust, practical decision support tools to facilitate diagnosis, assessment, and symptom monitoring to improve patient health and care.
We are a dynamic research group based at the Usher Institute, Edinburgh Medical School, University of Edinburgh working at the interface of engineering, mathematics, informatics, and medicine. We passionately believe we can design and provide effective solutions which will revolutionize contemporary healthcare delivery through capitalizing technology and harnessing data.
We develop and apply signal processing and statistical machine learning algorithms to explore data and decipher complicated concealed statistical relationships. Our algorithms are directly driven by and validated on complicated real-world problems, aiming to facilitate interpretation of the underlying key mechanisms of the modelled system. Our work is inherently multi-disciplinary and we collaborate with industrial partners and researchers worldwide. We tackle challenging problems in healthcare domains from neurodegenerative disorders and mental health, to asthma, cardiovascular disease, and neonatal monitoring.
Special issue in IEEE Journal of Biomedical Health Informatics for 2023
We are organizing a special issue in IEEE JBHI, where Thanasis serves as the Lead Guest Editor. This special issue invites extended versions of the best conference papers from the Pervasive Computing Technologies for Healthcare and is also an open call. See the call for papers and submit your best work!
Special issue in Sensors for 2023
We are organizing a special issue in Sensors, where Thanasis serves as the Lead Guest Editor. This special issue invites work broadly in smart sensing technologies. See the call for papers and submit your best work!
10 Jan 2023
Journal paper accepted in Sensors! We demonstrate, for the first time, that mining longitudinal continuous temperature data collected from a smartwatch can provide clinically useful information, using stroke as a testbed.