Andrew obtained his PhD (D.Phil) from the Institute of Biomedical Engineering (IBME) at the University of Oxford. His research focused on the development of digital biomarkers for remote patient monitoring using multimodal data from consumer wearable devices, such as smartphones and smartwatches.
Prior to his PhD, Andrew holds a bachelor’s degree (BAI, BA) in Biomedical Engineering (2015) and master’s degree (MAI) in Neural Engineering (2016) from Trinity College, the University of Dublin. During his time at Trinity, Andrew’s research investigated the use of machine learning techniques to predict the onset of dementia in later life, through the characterisation of gait and cognitive performance from routine clinical assessments conducted during the Irish Longitudinal Study on Aging (TILDA).
Andrew’s research at Oxford continues to develop digital biomarkers for people with neurodegenerative and autoimmune diseases. By applying advanced signal processing, machine- and deep-learning techniques, his work hopes to further understand and interpret the habitual pattern and function of these diseases, outside of the clinic.
- Digital Biomarkers
- Smartphone & smartwatch wearable sensors
- Remote Patient Monitoring
- Explainable AI (XAI)
Neurodegenerative and autoimmune diseases follow subtle and unpredictable trajectories with a high variability between patients and over time. It is therefore notoriously difficult to quantify effective therapeutic interventions and disease management techniques.
Andrew’s research aims to explore how we can capture digital biomarkers of disease, through continuously collecting smartphone and smartwatch measurements when patients are at-home. Clinical applications of machine learning (ML), often termed under the broader tag, artificial intelligence (AI), can act as powerful tools to learn complex and unseen digital patterns of disease.
The development of digital biomarkers, and remote patient monitoring through digital device measurements, could greatly augment routine healthcare assessments for people with these diseases; to help remotely monitor and identify signs of degeneration before they occur, and to understand new facets of habitual disease and disease phenotypes.
Andrew’s research is in collaboration with various pharmaceutical industrial partners, aimed at understanding how different patients respond to various treatments, and to create innovative ways to further drug discovery.
Digital Biomarkers Project
Analysing data to form digital biomarkers capable of measuring clinically meaningful differences in disease states/progression and quality of life in one of more clinical condition.
Andrew currently teaches the following undergraduate courses:
B18 Biomedical Modelling and Monitoring (A10589, 3rd Year Engineering Undergraduates)
(head demonstrator & tutor)
The B18 Biomedical Modelling and Monitoring (A10589) Wearables Practical Laboratory course material can be found at: https://github.com/apcreagh/B18-Wearables-Laboratory