This paper is in the following e-collection/theme issue:

Machine Learning (2032) Patient-Reported Outcome Measures (PROMs) (363) mHealth for Patient Education (289) Pulmonology, COPD (191)

Published on in Vol 10, No 3 (2022): March

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/26499, first published .
Prediction of Chronic Obstructive Pulmonary Disease Exacerbation Events by Using Patient Self-reported Data in a Digital Health App: Statistical Evaluation and Machine Learning Approach

Prediction of Chronic Obstructive Pulmonary Disease Exacerbation Events by Using Patient Self-reported Data in a Digital Health App: Statistical Evaluation and Machine Learning Approach

Prediction of Chronic Obstructive Pulmonary Disease Exacerbation Events by Using Patient Self-reported Data in a Digital Health App: Statistical Evaluation and Machine Learning Approach

Authors of this article:

Francis P Chmiel1 Author Orcid Image ;   Dan K Burns1 Author Orcid Image ;   John Brian Pickering1 Author Orcid Image ;   Alison Blythin2 Author Orcid Image ;   Thomas MA Wilkinson2, 3, 4 Author Orcid Image ;   Michael J Boniface1 Author Orcid Image
Article Authors Cited by (23) Tweetations (2) Metrics

Francis P Chmiel   1 , MSc, DPhil ;   Dan K Burns   1 , MSc, PhD ;   John Brian Pickering   1 , DPhil ;   Alison Blythin   2 , MRES ;   Thomas MA Wilkinson   2, 3, 4 * , PhD ;   Michael J Boniface   1 * , BEng

1 School of Electronics and Computer Science, University of Southampton, Southampton, United Kingdom

2 my mHealth Limited, Bournemouth, United Kingdom

3 National Institute for Health Research Applied Research Collaboration Wessex, University of Southampton, Southampton, United Kingdom

4 Faculty of Medicine, University of Southampton, Southampton, United Kingdom

*these authors contributed equally

Corresponding Author:

  • Francis P Chmiel, MSc, DPhil
  • School of Electronics and Computer Science
  • University of Southampton
  • University Road
  • Southampton SO17 1BJ
  • United Kingdom
  • Phone: 44 023 8059 8866
  • Email: F.P.Chmiel@soton.ac.uk