Andrew P. Creagh

Institute of Biomedical Engineering (IBME),

Old Road Campus Research Building (ORCRB),

University of Oxford,

OX37DQ, UK.

Welcome

I am a Digital Health & AI/ML Scientist. I am currently the Head of AI/ML at Sanome, where we’re building personalised AI-powered early warning systems to predict individual health outcomes.

I am concurrently a Visiting Researcher at the Computational Health Informatics (CHI) laboratory at the University of Oxford, a Junior Research Fellow at St Cross College, University of Oxford, and a Visiting Researcher at the Oxford Wearables Laboratory at the Big Data Institute (BDI).

My Research Profiles:

Sanome
Big Data Institute
St Cross College

Research

My research aims to explore how we can capture digital biomarkers of disease using multimodal digital health data and AI/ML to inform early warning of clinical events. I have a specific interest in how the integration of digital health technologies (DHT)—like smartwatches and wearable sensors—will augment routinely collected clinical data and electronic health records (EHR) to enhance patient treatment strategies, uncover digital phenotypes and ultimately develop personalised patient monitoring stratagies and truly personalised healthcare for patients.

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. My technical areas of expertise include AI/ML for disease prediction, explainable AI (XAI), and time-series analysis. I have a specific interest in creating interpretable, robust, and validated digital biomarkers 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.

Following my DPhil, I was a GSK Postdoctoral Fellow in Digital Biomarkers and a Postdoctoral Researcher at the Computational Health Informatics (CHI) laboratory at the University of Oxford led by Prof. David A. Clifton, as well as the Oxford Wearables Laboratory led by Prof. Aiden Doherty at the Big Data Institute (BDI), University of Oxford.

My research explored the development of validated digital biomarkers for clinical trial (CT) end-points and prescribed digital therapeutics (DTx) using consumer digital health technologies (DHT) collected from smartphone and smartwatch app. sensors. I have worked with CT data from patients in a range of Therapeutic Areas (TA) from multiple sclerosis to Parkinson’s disease, rheumatoid arthritis, dementia, and mental health.

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

May 20, 2023	Excited to be speaking at the upcoming Pint of Science Festival in Oxford. I’m going to try debunk the 10,000 steps a day myth! ⌚
Mar 1, 2023	Check out my latest blog post, where I summarise our recent work on ‘Synthesizing Mixed-type Electronic Health Records using Diffusion Models’, which is now available on arXiv! 📢
Jan 12, 2023	Excited to be speaking at the upcoming IoT Clinical Trials Europe conference in London, where I will discuss the opportunities for digital health technologies (📱/⌚) and AI/ML to augment decentralised clinical trials 👥📢
Jan 5, 2023	In in my latest blog post, I discuss what we learned in our new journal paper, which was recently published in IEEE OJEMB 💡
Nov 23, 2022	Check out our latest blog post, where we discuss the key findings, learnings, and impact of the WeaRAble-PRO study in RA 💡
Nov 19, 2022	Our new preprint: “Digital health technologies and machine learning augment patient reported outcomes to remotely characterise rheumatoid arthritis” is now available on medrXiv 📢.
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

Digital health technologies and machine learning augment patient reported outcomes to remotely characterise rheumatoid arthritis

A. P. Creagh, V. Hamy, H. Yuan, G. Mertes, R. Tomlinson, W-H. Chen, R. Williams, C. Llop, C. Yee, M-H. Duh, A. Doherty, L. Garcia-Gancedo, and D. A. Clifton

medrXiv 2022

Bib HTML PDF

@article{creagh2022digital,
  bibtex_show = {true},
  title = {Digital health technologies and machine learning augment patient reported outcomes to remotely characterise rheumatoid arthritis},
  author = {Creagh, A. P. and Hamy, V. and Yuan, H. and Mertes, G. and Tomlinson, R. and Chen, W-H. and Williams, R. and Llop, C. and Yee, C. and Duh, M-H. and Doherty, A. and Garcia-Gancedo, L. and Clifton, D. A.},
  elocation-id = {2022.11.18.22282305},
  year = {2022},
  doi = {10.1101/2022.11.18.22282305},
  publisher = {Cold Spring Harbor Laboratory Press},
  html = {https://www.medrxiv.org/content/10.1101/2022.11.18.22282305v1},
  journal = {medrXiv},
  abbr = {medrXiv},
  pdf = {Creagh2022_DHTAugmentPROinRA.pdf},
  selected = {true}
}

IEEE OJEMB

Longitudinal Trend Monitoring of Multiple Sclerosis Ambulation using Smartphones

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

IEEE Open Journal of Engineering in Medicine and Biology 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://ieeexplore.ieee.org/document/9944841},
  journal = {IEEE Open Journal of Engineering in Medicine and Biology},
  abbr = {IEEE OJEMB},
  pdf = {Creagh_2022_longitudinal-trend-monitoring.pdf},
  selected = {true}
}

npj Sci. Reports

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

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

Nature Scientific Reports 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 = {Nature 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},
  abbr = {npj 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

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

IEEE Journal of Biomedical Health Informatics 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},
  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

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

Physiological Measurement 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},
  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

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

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}
}