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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 SchoolUniversity 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.

News

24 June 2020


New journal article accepted for publication in the British Journal of Clinical Pharmacology

"Measuring and reporting treatment adherence: what can we learn by comparing two respiratory conditions?". This study investigated medication non-adherence and its longer-term effects in terms of side-effects, morbidity, and mortality. We used as testbeds two respiratory conditions, asthma and tuberculosis, to cross-reference approaches towards assessing adherence, and making recommendations for effective adherence reporting.

24 June 2020


New journal article accepted for publication in the Journal of Child Psychology and Psychiatry

"Eye-tracking for longitudinal assessment of social cognition in children born preterm". We have monitored preterm-born and term-born children to investigate social attentional preference in infancy and at 5 years, its relationship with neurodevelopment, and the influence of socioeconomic deprivation.

27 May 2020


New journal article accepted for publication in Neurocomputing

"Artificial intelligence within the interplay between natural and artificial Computation: advances in data science, trends and applications". This comprehensive study from an inter-disciplinary international team summarizes many of the recent advances in data science and artificial intelligence within the interplay between natural and artificial computation.

8 May 2020


New journal article accepted for publication in Stroke

"Telemedicine cognitive hebavioural therapy for anxiety after stroke: proof of concept randomized controlled trial". Anxiety affects a large proportion of stroke survivors. We developed a telemedicine-based approach for delivering guided self-help cognitive behavioural therapy for anxiety after stroke. The study also explored the use of objective assessment using wrist-worn actigraphy to complement patient reported outcome measures.

2 March 2020


New journal article accepted for publication in JMIR mHealth and uHealth

"Objective characterization of activity, sleep, and circadian variability patterns using a wrist-worn activity sensor: insights into post-traumatic stress disorder". This study provides new approaches towards visualizing, extracting patterns, and interpreting findings to characterize actigraphy data. The application is on post-traumatic stress disorder, but the algorithmic tools can be applied in any setting where we passively record actigraphy signals using wrist-worn wearables.

11 February 2020


New journal article accepted for publication in JMIR Medical Informatics

"Challenges of clustering multimodal clinical data: a review of applications in asthma subtyping" investigates common pitfalls in the application of clustering methodologies in clinical settings, using asthma as an exemplar. The manuscript makes a strong case for careful algorithmic considerations based on data variable type, sample size, and more general issues that appear in multimodal clinical datasets when attempting to infer data properties through clustering.

19 January 2020


Four new conference papers accepted for publication in BioSignals (BIOSTEC 2020)

Thanasis is co-organising a special session 'SERPICO' as part of the 13th Internatonal Joint Conference on Biomedical Engineering Systems and Technologies, and four members of the group (Andres, Evi, Minhong, Thanasis) will be presenting work across different topics. Andres and Thanasis present findings on Parkinson's disease, Evi presents work from her PhD on neonatal monitoring, and Minhong presents her latest findings on stem cell pattern formation.

Previous years in review
2019
2018

© Data Analytics Research and Technology in Healthcare (DARTH) group, 2020