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

Clinical Information and Decision Making (1915) Registered Report (791) Machine Learning (2076) Pulmonology, COPD (195) Decision Support for Health Professionals (1539) Clinical Informatics (1325) Tools, Programs and Algorithms (376)

Published on in Vol 24, No 1 (2022): January

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/28953, first published .
Developing a Machine Learning Model to Predict Severe Chronic Obstructive Pulmonary Disease Exacerbations: Retrospective Cohort Study

Developing a Machine Learning Model to Predict Severe Chronic Obstructive Pulmonary Disease Exacerbations: Retrospective Cohort Study

Developing a Machine Learning Model to Predict Severe Chronic Obstructive Pulmonary Disease Exacerbations: Retrospective Cohort Study

Authors of this article:

Siyang Zeng1 Author Orcid Image ;   Mehrdad Arjomandi2, 3 Author Orcid Image ;   Yao Tong1 Author Orcid Image ;   Zachary C Liao1 Author Orcid Image ;   Gang Luo1 Author Orcid Image
Article Authors Cited by (26) Tweetations (13) Metrics

Journals

  1. Wang D, Li Y, Mao Y, He S, Zhu P, Wei Q. A machine-learning approach for predicting the effect of carnitine supplementation on body weight in patients with polycystic ovary syndrome. Frontiers in Nutrition 2022;9 View
  2. Zeng S, Arjomandi M, Luo G. Automatically Explaining Machine Learning Predictions on Severe Chronic Obstructive Pulmonary Disease Exacerbations: Retrospective Cohort Study. JMIR Medical Informatics 2022;10(2):e33043 View
  3. 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
  4. 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
  5. Zhuan B, Ma H, Zhang B, Li P, Wang X, Yuan Q, Yang Z, Xie J. Identification of non-small cell lung cancer with chronic obstructive pulmonary disease using clinical symptoms and routine examination: a retrospective study. Frontiers in Oncology 2023;13 View
  6. El-Sherbini A, Hassan Virk H, Wang Z, Glicksberg B, Krittanawong C. Machine-Learning-Based Prediction Modelling in Primary Care: State-of-the-Art Review. AI 2023;4(2):437 View
  7. Smith L, Oakden-Rayner L, Bird A, Zeng M, To M, Mukherjee S, Palmer L. Machine learning and deep learning predictive models for long-term prognosis in patients with chronic obstructive pulmonary disease: a systematic review and meta-analysis. The Lancet Digital Health 2023;5(12):e872 View
  8. 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
  9. Al-Anazi S, Al-Omari A, Alanazi S, Marar A, Asad M, Alawaji F, Alwateid S. Artificial intelligence in respiratory care: Current scenario and future perspective. Annals of Thoracic Medicine 2024;19(2):117 View
  10. Wang N, Li M, Wang G, Lv L, Yu X, Cheng X, Liu T, Ji W, Hu T, Shi Z. Development and validation of a nomogram for assessing survival in acute exacerbation of chronic obstructive pulmonary disease patients. BMC Pulmonary Medicine 2024;24(1) View
  11. Wu Y, Xia S, Liang Z, Chen R, Qi S. Artificial intelligence in COPD CT images: identification, staging, and quantitation. Respiratory Research 2024;25(1) 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. Arnold M, Liou L, Boland M. Development, evaluation and comparison of machine learning algorithms for predicting in-hospital patient charges for congestive heart failure exacerbations, chronic obstructive pulmonary disease exacerbations and diabetic ketoacidosis. BioData Mining 2024;17(1) View
  14. Atzeni M, Cappon G, Quint J, Kelly F, Barratt B, Vettoretti M. A machine learning framework for short-term prediction of chronic obstructive pulmonary disease exacerbations using personal air quality monitors and lifestyle data. Scientific Reports 2025;15(1) View
  15. Camacho-Magriñán P, Sales-Lerida D, León-Jiménez A, Sanchez-Morillo D. Indoor Environmental Monitoring and Chronic Respiratory Diseases: A Systematic Review. Technologies 2025;13(3):122 View
  16. Shah S. Enhancing COPD Care for Women: A Predictive Tool for Palliative Needs. Respirology 2025;30(7):547 View
  17. Zhou C, Shuai L, Hu H, Ung C, Lai Y, Fan L, Du W, Wang Y, Li M. Applications of machine learning approaches for pediatric asthma exacerbation management: a systematic review. BMC Medical Informatics and Decision Making 2025;25(1) View
  18. Pant S, Yang H, Cho S, Ryu E, Choi J. Development of a deep learning model to predict smoking status in patients with chronic obstructive pulmonary disease: A secondary analysis of cross-sectional national survey. DIGITAL HEALTH 2025;11 View
  19. Zhang Y, Chen H, Hu S, Chen C, Chen W. Construction and validation of a nomogram prediction model for death risk in patients with chronic obstructive pulmonary disease complicated by hypercapnic respiratory failure in the intensive care unit. Respiratory Medicine 2025;245:108188 View
  20. Rodríguez-Pérez J, Andreu-Martínez R, Daza R, Fernández-Arroyo L, Hernández-García A, Díaz-García E, Cubillos-Zapata C, Lozano-Diez A, Morales A, Ramos D, Aragonés J, Cogolludo Á, del Peso L, García-Río F, Calzada M. Oxidative Stress and Inflammation in Hypoxemic Respiratory Diseases and Their Comorbidities: Molecular Insights and Diagnostic Advances in Chronic Obstructive Pulmonary Disease and Sleep Apnea. Antioxidants 2025;14(7):839 View
  21. Shao X, Zhang L, Wang Y, Ying Y, Chen X. Developing an interpretable machine learning predictive model of chronic obstructive pulmonary disease by serum PFAS concentration. Frontiers in Public Health 2025;13 View
  22. Maya Viejo J, Navarro Ros F. Automated Chronic Obstructive Pulmonary Disease Phenotyping and Control Assessment in Primary Care: Retrospective Multicenter Study Using the Seleida Model. JMIR Medical Informatics 2025;13:e74932 View
  23. Iannone S, Kaur A, Johnson K. Artificial Intelligence in Outpatient Primary Care: A Scoping Review on Applications, Challenges, and Future Directions. Journal of General Internal Medicine 2025 View

Books/Policy Documents

  1. Cano I, Arismendi E, Borrat X. Digital Respiratory Healthcare. View
  2. Saini D, Kushwaha S. Data Science and Applications. View
  3. Suggala R, Vadrevu P, Adusumalli S, Nagaraju D, Gudala R, Bunga S. Data Processing and Networking. View