Andrew P. Creagh

Institute of Biomedical Engineering (IBME),

Old Road Campus Research Building (ORCRB),

University of Oxford,

OX37DQ, UK.

Welcome

I am a Postdoctoral Researcher at the Computational Health Informatics (CHI) laboratory at the University of Oxford led by Prof. David A. Clifton.

I am concurrently a GSK Postdoctoral Fellow in Digital Biomarkers, a Junior Research Fellow at St Cross College, University of Oxford, and a Postdoctoral Researcher at the Wearables Laboratory led by Prof. Aiden Doherty at the Big Data Institute (BDI), University of Oxford.

Links:

My Departmental Website
My College Website

Research

My research aims to explore how we can capture digital biomarkers of disease using counsumer digital health technologies (DHT)—by continuously collecting smartphone and smartwatch measurements when patients are at-home. I work mainly with patients who have neurdegenerative and autoimmune diseases, such as multiple sclerosis, rheumatoid arthritis, Parkinson’s disease and dementia.

Clinical applications of AI/ML (machine learning) can act as powerful tools to learn complex and unseen digital patterns of disease, to help remotely monitor and identify signs of degeneration before they occur, and to understand new facets of habitual disease and disease phenotypes. I have a specific interest in creating interpretable, robust, and transparent models through explainable AI (XAI) frameworks.

Background

I obtained my DPhil. (PhD) in Clinical Machine Learning at the University of Oxford, developing digital biomarkers in collaboration with industrial partners, F. Hoffmann-La Roche.

Prior to my DPhil. I hold a bachelor’s degree (BAI, BA) in Biomedical Engineering and master’s degree (MAI) in Neural Engineering from Trinity College, the University of Dublin. My research at Trinity 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).

news

Aug 23, 2022	We have now publicly released our code for our paper: “Mixture of Input-Output Hidden Markov Models for Heterogeneous Disease Progression Modeling” from ICML on GitHub .
Jun 18, 2022	Our new preprint: “Self-supervised Learning for Human Activity Recognition Using 700,000 Person-days of Wearable Data” is now available on arXiv 📢.
Feb 24, 2022	Our new preprint: “Longitudinal Trend Monitoring of Multiple Sclerosis Ambulation using Smartphones” is now available on medrXiv 📢.
Nov 1, 2021	Announced as this year's recipient of the prestigious IET William James Award 🥳.
Jul 15, 2021	Our new explainable AI (XAI) paper is now available online in Nature Scientific Reports 🥳.
Jun 16, 2021	Awarded Junior Research Fellowship (JRF) St Cross College, University of Oxford.
Apr 12, 2021	Awarded STEM for Britain Medal at the UK Houses of Parliament
Dec 21, 2020	Our Draw a Shape paper was the most cited paper in Physiological Measurements in 2020!

selected publications

medrXiv
Longitudinal Trend Monitoring of Multiple Sclerosis Ambulation using Smartphones

Creagh, A. P., Dondelinger, F., Lipsmeier, F., Lindemann, M., and De Vos, M.

medrXiv 2022

Bib HTML PDF
@article{Creagh2022Longitudinal, bibtex_show = {true}, author = {Creagh, A. P. and Dondelinger, F. and Lipsmeier, F. and Lindemann, M. and De Vos, M.}, title = {Longitudinal Trend Monitoring of Multiple Sclerosis Ambulation using Smartphones}, elocation-id = {2022.02.21.22270745}, year = {2022}, doi = {10.1101/2022.02.21.22270745}, publisher = {Cold Spring Harbor Laboratory Press}, html = {https://www.medrxiv.org/content/early/2022/02/24/2022.02.21.22270745}, journal = {medrXiv}, abbr = {medrXiv}, pdf = {Creagh2022_LongitudinalTrendMonitoring_medrXiv.pdf}, selected = {true} }

Sci. Reports

Interpretable deep learning for the remote characterisation of ambulation in multiple sclerosis using smartphones

Creagh, A. P., Lipsmeier, F., Lindemann, M., and De Vos, M.

2021

Bib HTML PDF

@article{Creagh2021InterpretableDeepLearning,
  bibtex_show = {true},
  author = {Creagh, A. P. and Lipsmeier, F. and Lindemann, M. and De Vos, M.},
  title = {Interpretable deep learning for the remote characterisation of ambulation in multiple sclerosis using smartphones},
  journal = {Scientific Reports},
  volume = {11},
  number = {1},
  pages = {14301},
  issn = {2045-2322},
  doi = {10.1038/s41598-021-92776-x},
  url = {https://doi.org/10.1038/s41598-021-92776-x},
  html = {https://www.nature.com/articles/s41598-021-92776-x},
  year = {2021},
  type = {Journal Article},
  abbr = {Sci. Reports},
  pdf = {Creagh_2021_interpretable-dl-ambulation.pdf},
  selected = {true}
}

IEEE J-BHI
Smartphone- and Smartwatch-Based Remote Characterisation of Ambulation in Multiple Sclerosis During the Two-Minute Walk Test

Creagh, A. P., Simillion, C., Bourke, A. K., Scotland, A., Lipsmeier, F., Bernasconi, C., Beek, J., Baker, M., Gossens, C., Lindemann, M., and De Vos, M.

2021

Bib HTML PDF
@article{Creagh2020SmartphoneAmbulation, bibtex_show = {true}, author = {Creagh, A. P. and Simillion, C. and Bourke, A. K. and Scotland, A. and Lipsmeier, F. and Bernasconi, C. and van Beek, J. and Baker, M. and Gossens, C. and Lindemann, M. and De Vos, M.}, title = {Smartphone- and Smartwatch-Based Remote Characterisation of Ambulation in Multiple Sclerosis During the Two-Minute Walk Test}, journal = {IEEE Journal of Biomedical Health Informatics}, volume = {25}, number = {3}, pages = {838-849}, issn = {2168-2208 (Electronic) 2168-2194}, doi = {10.1109/JBHI.2020.2998187}, url = {https://www.ncbi.nlm.nih.gov/pubmed/32750915}, html = {https://ieeexplore.ieee.org/document/9103259}, year = {2021}, type = {Journal Article}, abbr = {IEEE J-BHI}, pdf = {Creagh_2020_smartphone_ambulation.pdf}, selected = {true} }
Physiol. Meas.
Smartphone-based remote assessment of upper extremity function for multiple sclerosis using the Draw a Shape Test

Creagh, A. P., Simillion, C., Scotland, A., Lipsmeier, F., Bernasconi, C., Belachew, S., Beek, J., Baker, M., Gossens, C., Lindemann, M., and De Vos, M.

2020

Bib HTML PDF
@article{Creagh2020DrawAShape, bibtex_show = {true}, author = {Creagh, A. P. and Simillion, C. and Scotland, A. and Lipsmeier, F. and Bernasconi, C. and Belachew, S. and van Beek, J. and Baker, M. and Gossens, C. and Lindemann, M. and De Vos, M.}, title = {Smartphone-based remote assessment of upper extremity function for multiple sclerosis using the Draw a Shape Test}, journal = {Physiological Measurement}, volume = {41}, number = {5}, pages = {054002}, issn = {1361-6579}, doi = {10.1088/1361-6579/ab8771}, url = {http://dx.doi.org/10.1088/1361-6579/ab8771}, html = {https://iopscience.iop.org/article/10.1088/1361-6579/ab8771}, year = {2020}, type = {Journal Article}, abbr = {Physiol. Meas.}, pdf = {Creagh_2020_draw-a-shape.pdf}, selected = {true} }
arXiv
Self-supervised Learning for Human Activity Recognition Using 700,000 Person-days of Wearable Data

Yuan, H., Chan, S., Creagh, A. P., Tong, C., Clifton, D. A., and Doherty, A.

arXiv preprint 2022

Bib HTML
@article{yuan2022self, bibtex_show = {true}, title = {Self-supervised Learning for Human Activity Recognition Using 700,000 Person-days of Wearable Data}, author = {Yuan, H. and Chan, S. and Creagh, A. P. and Tong, C. and Clifton, D. A. and Doherty, A.}, journal = {arXiv preprint}, year = {2022}, selected = {true}, abbr = {arXiv}, doi = {10.48550/arXiv.2206.02909}, url = {https://doi.org/10.48550/arXiv.2206.02909}, html = {https://doi.org/10.48550/arXiv.2206.02909} }