Dr. Andrew P. Creagh is currently a Postdoctoral Researcher at the Computational Health Informatics (CHI) laboratory, Institute of Biomedical Engineering (IBME), at the University of Oxford. He is concurrently a Junior Research Fellow at St. Cross College, University of Oxford and a Postdoctoral Fellow at GSK, London.
Andrew obtained a DPhil. (PhD) in Clinical Machine Learning from the University of Oxford, for his work developing digital biomarkers in collaboration with pharmaceutical partners, F. Hoffmann-La Roche. Previously, Andrew was awarded his bachelor’s degrees (BAI, BA) and Master’s degree (MAI) in Biomedical Engineering from Trinity College, the University of Dublin.
Andrew’s research aims to explore how “digital biomarkers” of disease can be captured for people who have neurodegenerative and autoimmune conditions, through continuously collecting smartphone and smartwatch measurements from patients at home. Transforming these data streams though advanced machine- and deep-learning techniques enable the learning of complex and unseen digital patterns of disease, to help remotely monitor and identify signs of degeneration before they occur, and ultimately to create innovative ways to further drug discovery and better treatment regimes.
- 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.
EPSRC Centre for Doctoral Training (CDT) in Health Data Science (HDS)
Machine Learning for Time-Series (2021-2022)
1st Year PhD Postgraduate CDT Students:
- Lecturer: machine and deep learning for time-series
- Course designer: machine and deep learning for time-series practical labs
The CDT HDS machine-learning for time-series course material can be found at https://github.com/apcreagh/CDTworkshop_ML4timeseries/
Department of Engineering Science
B18 Biomedical Modelling and Monitoring (2017-2021)
3rd Year Engineering Undergraduate students:
- 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