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

Machine Learning (1501) Patient-Reported Outcome Measures (PROMs) (294) mHealth for Patient Education (240) Pulmonology, COPD (145)

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 (15) Tweetations (2) Metrics

Journals

  1. Lee S, Yoon S, Lee M, Kim H, Lim Y, Park H, Park S, Jeong S, Han H. Health-Screening-Based Chronic Obstructive Pulmonary Disease and Its Effect on Cardiovascular Disease Risk. Journal of Clinical Medicine 2022;11(11):3181 View
  2. Guo M, Yu C, Li Z, Hashmi M. [Retracted] The Efficacy and Safety of Budesonide/Glycopyrronium/Formoterol in the Treatment of COPD in the Elderly. Contrast Media & Molecular Imaging 2022;2022(1) View
  3. Yan X, Zhang Y, Huang M, Yang X, Yan Y, Hu F. Graph-based medicine embedding learning via multiple attentions. Computers and Electrical Engineering 2023;105:108494 View
  4. Duckworth C, Boniface M, Kirk A, Wilkinson T. Exploring the Validity of GOLD 2023 Guidelines: Should GOLD C and D Be Combined?. International Journal of Chronic Obstructive Pulmonary Disease 2023;Volume 18:2335 View
  5. Jacobson P, Lind L, Persson H. Unleashing the Power of Very Small Data to Predict Acute Exacerbations of Chronic Obstructive Pulmonary Disease. International Journal of Chronic Obstructive Pulmonary Disease 2023;Volume 18:1457 View
  6. Alghamdi S. Content, Mechanism, and Outcome of Effective Telehealth Solutions for Management of Chronic Obstructive Pulmonary Diseases: A Narrative Review. Healthcare 2023;11(24):3164 View
  7. Duckworth C, Cliffe B, Pickering B, Ainsworth B, Blythin A, Kirk A, Wilkinson T, Boniface M. Characterising user engagement with mHealth for chronic disease self-management and impact on machine learning performance. npj Digital Medicine 2024;7(1) View
  8. D’Cruz R, Hart N. A history of home mechanical ventilation: The past, present and future. Chronic Respiratory Disease 2024;21 View
  9. Wang S, Li W, Zeng N, Xu J, Yang Y, Deng X, Chen Z, Duan W, Liu Y, Guo Y, Chen R, Kang Y. Acute exacerbation prediction of COPD based on Auto-metric graph neural network with inspiratory and expiratory chest CT images. Heliyon 2024;10(7):e28724 View
  10. Miller S, Nichols M, Teufel II R, Silverman E, Walentynowicz M. Use of Ecological Momentary Assessment to Measure Dyspnea in COPD. International Journal of Chronic Obstructive Pulmonary Disease 2024;Volume 19:841 View
  11. Glyde H, Morgan C, Wilkinson T, Nabney I, Dodd J. Remote Patient Monitoring and Machine Learning in Acute Exacerbations of Chronic Obstructive Pulmonary Disease: Dual Systematic Literature Review and Narrative Synthesis. Journal of Medical Internet Research 2024;26:e52143 View
  12. Kalaiyarasan K, Sridhar R. Artificial Intelligence in Respiratory Medicine. Journal of Association of Pulmonologist of Tamil Nadu 2023;6(2):53 View

Books/Policy Documents

  1. Anshari M, Hamdan M, Ali E, Ahmad N. Perspectives on the Transition Toward Green and Climate Neutral Economies in Asia. View
  2. Upson S, Kusupati V, Bime C. Comprehensive Precision Medicine. View