Andrew P. Creagh

prof_pic.jpg

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 Oxford Wearables Laboratory led by Prof. Aiden Doherty at the Big Data Institute (BDI), University of Oxford.

My Research Profiles:

Dept. of Engineering Science Website
Big Data Institute Website
St Cross College Website

Research

My research aims to explore how we can capture digital biomarkers of disease using consumer digital health technologies (DHT)—collected from smartphone and smartwatch app. sensors and patient reported outcomes (PROs) when patients are at home—to develop personalised monitoring, enhance patient treatment strategies, and ultimately the development of validated digital biomarkers for clinical trial (CT) end-points and prescribed digital therapeutics (DTx). I have worked with CT data from patients in a range of Therapeutic Areas (TA) from multiple sclerosis to Parkinson’s disease, rheumatoid arthritis 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. 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.

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

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

  1. 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
  2. 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
  3. 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
  4. 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
  5. 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
  6. 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