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

Machine Learning (1971) Patient-Reported Outcome Measures (PROMs) (350) mHealth for Patient Education (285) Pulmonology, COPD (185)

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 (22) 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
  13. Chen Z, Hao J, Sun H, Li M, Zhang Y, Qian Q. Applications of digital health technologies and artificial intelligence algorithms in COPD: systematic review. BMC Medical Informatics and Decision Making 2025;25(1) View
  14. Moraza J, Esteban-Aizpiri C, Aramburu A, García P, Sancho F, Resino S, Chasco L, Conde F, Gutiérrez J, Santano D, Esteban C. Using machine learning to predict deterioration of symptoms in COPD patients within a telemonitoring program. Scientific Reports 2025;15(1) View
  15. Kronborg T, Hangaard S, Laursen S, Hæsum L, Egmose J, Bender C, Secher P, Hejlesen O, Udsen F. Impact of Telemonitoring With Exacerbation Prediction Algorithm Versus Telemonitoring Alone on Hospitalizations and Health-Related Quality of Life in Patients With COPD. Respiratory Care 2025;70(8):954 View
  16. Tang X. Efficacy of remote respiratory rehabilitation in stable chronic obstructive pulmonary disease and factors affecting acute exacerbation. American Journal of Translational Research 2025;17(3):2166 View
  17. Wójcik Z, Dimitrova V, Warrington L, Velikova G, Absolom K. Using artificial intelligence to predict patient outcomes from patient-reported outcome measures: a scoping review. Health and Quality of Life Outcomes 2025;23(1) View
  18. Park H, Kong S, Lee M, Ryu H, Hamakawa Y, Luppi F, Leung J. Digital health technologies for improving the management of people with chronic obstructive pulmonary disease. Frontiers in Digital Health 2025;7 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
  3. Suggala R, Vadrevu P, Adusumalli S, Nagaraju D, Gudala R, Bunga S. Data Processing and Networking. View

Conference Proceedings

  1. Raj M, Malardhas J, Devapriya I. 2024 10th International Conference on Communication and Signal Processing (ICCSP). Machine Learning Approach To Predict Multiple Diseases Based On Symptoms View