Published on in Vol 26 (2024)

This is a member publication of University of Bristol (Jisc)

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/52143, first published .
Remote Patient Monitoring and Machine Learning in Acute Exacerbations of Chronic Obstructive Pulmonary Disease: Dual Systematic Literature Review and Narrative Synthesis

Remote Patient Monitoring and Machine Learning in Acute Exacerbations of Chronic Obstructive Pulmonary Disease: Dual Systematic Literature Review and Narrative Synthesis

Remote Patient Monitoring and Machine Learning in Acute Exacerbations of Chronic Obstructive Pulmonary Disease: Dual Systematic Literature Review and Narrative Synthesis

Journals

  1. 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
  2. Ullah S, Khan S, Vanecek D, Ur Rehman I. Machine Learning and Digital-Twins-Based Internet of Robotic Things for Remote Patient Monitoring. IEEE Access 2025;13:57141 View
  3. Feng Y, Jiang F, Lu D, Yang L, Zhang Q, Yang H, Li N, Jiang Y. Predicting chronic obstructive pulmonary disease (COPD) with optimized machine learning via leveraging comparative analysis of XGBoost and catboost. Journal of Ambient Intelligence and Humanized Computing 2025;16(4-5):613 View
  4. Hu C, Liao X, Fang Y, Zhu S, Lan X, Cheng G. Clinical and Cost-Effectiveness of Telehealth-Supported Home Oxygen Therapy on Adherence, Hospital Readmission, and Health-Related Quality of Life in Patients With Chronic Obstructive Pulmonary Disease: Systematic Review and Meta-Analysis of Randomized Controlled Trials. Journal of Medical Internet Research 2025;27:e73010 View
  5. Sánchez F, Bommatty M, Haro M, Ruiz F, del Mar Pérez Luque M, Bernáldez C, Montiel A, Gil V, Rodríguez F, Rueda D, Martinez M, López E, Escribano dueñas A, Baptista F. Telemonitoring in patients with COPD: A prospective study with results from the AIRE project. Respiratory Medicine 2025;248:108307 View
  6. Cherukuvada S, Chaitanya R, Janardhan M, Yara S, Shareef S, Harshini M, Kocherla R. A novel ensemble deep learning framework with spatial attention and high-order pooling for COPD detection. Discover Computing 2025;28(1) View
  7. McCabe S, Madiraca J, Cole L, Morgan E, Fowler T, Smith W, O’Connor Durham C, Lindell K, Miller S. Clinical Provider Perspectives on Remote Spirometry and mHealth for COPD. Nursing Reports 2025;15(11):402 View
  8. Pozza M, Navarin N, Sakkalis V, Gabrielli S. Artificial Intelligence Methods and Digital Intervention Strategies for Predicting and Managing Chronic Obstructive Pulmonary Disease Exacerbations: An Umbrella Review. Healthcare 2025;13(23):3037 View
  9. Sun Z, Li H, Zheng Y, Jia X, Ma J, Liu H, Yu X, Wang L, Li Y, Zhang B. Interpretable machine learning model based on multimodal ultrasound for bedside diagnosis of acute exacerbations in COPD. Respiratory Research 2025;26(1) View
  10. Kauzlaricova T, Augustynek M, Kubicek J. Advancements in Noninvasive Blood Pressure Measurement: A Comprehensive Review of Cuffed, Cuffless, and Contactless Methods. IEEE Transactions on Instrumentation and Measurement 2026;75:1 View
  11. Rafał Pelczar , Paulina Dybiak , Paweł Słoma , Adrian Morawiec , Maciej Zachara , Mateusz Bartoszek , Patryk Harnicki , Mikołaj Grodzki , Jakub Minas , Erwin Grzegorzak , Julia Florek , Oliwia Krawczyk . THE IMPACT OF VITAL SIGN MONITORING DEVICES ON THE QUALITY AND SAFETY OF CARE FOR OLDER ADULTS. International Journal of Innovative Technologies in Social Science 2026;(1(49)) View
  12. Montenegro M, Gielen J, Wang C, Vanrumste B, Ruttens D, Knevels R, Aerts J. AI and IoT for COPD Remote Monitoring: A Systematic Review of ECOPD Prediction Frameworks and Key Monitoring Physiological Variables (Preprint). JMIR Medical Informatics 2025 View