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

Wearable Devices and Sensors (1133) mHealth for Symptom and Disease Monitoring, Chronic Disease Management (1732) Ubiquitous Health (uHealth) (134) Machine Learning (2076) Decision Support for Health Professionals (1539) Clinical Information and Decision Making (1915) Pulmonology, COPD (195) Environmental Health (123) Artificial Intelligence (2129) Fitness Trackers and Smart Pedometers/Accelerometers (665)

Published on in Vol 9, No 5 (2021): May

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/22591, first published .
Acute Exacerbation of a Chronic Obstructive Pulmonary Disease Prediction System Using Wearable Device Data, Machine Learning, and Deep Learning: Development and Cohort Study

Acute Exacerbation of a Chronic Obstructive Pulmonary Disease Prediction System Using Wearable Device Data, Machine Learning, and Deep Learning: Development and Cohort Study

Acute Exacerbation of a Chronic Obstructive Pulmonary Disease Prediction System Using Wearable Device Data, Machine Learning, and Deep Learning: Development and Cohort Study

Authors of this article:

Chia-Tung Wu1 Author Orcid Image ;   Guo-Hung Li2 Author Orcid Image ;   Chun-Ta Huang3 Author Orcid Image ;   Yu-Chieh Cheng2 Author Orcid Image ;   Chi-Hsien Chen4 Author Orcid Image ;   Jung-Yien Chien3 Author Orcid Image ;   Ping-Hung Kuo3 Author Orcid Image ;   Lu-Cheng Kuo3 Author Orcid Image ;   Feipei Lai1 Author Orcid Image
Article Authors Cited by (100) Tweetations (4) Metrics

Journals

  1. Liao K, Liu C, Chen C, Shen Y. Machine Learning Approaches for Predicting Acute Respiratory Failure, Ventilator Dependence, and Mortality in Chronic Obstructive Pulmonary Disease. Diagnostics 2021;11(12):2396 View
  2. De Ramón Fernández A, Ruiz Fernández D, Gilart Iglesias V, Marcos Jorquera D. Analyzing the use of artificial intelligence for the management of chronic obstructive pulmonary disease (COPD). International Journal of Medical Informatics 2022;158:104640 View
  3. Zazo-Manzaneque R, Pons-Beltrán V, Vidaurre A, Santonja A, Sánchez-Díaz C. Classification Predictive Model for Air Leak Detection in Endoworm Enteroscopy System. Sensors 2022;22(14):5211 View
  4. Park Y, Go T, Hong S, Kim S, Han J, Kang Y, Kang D. Digital Biomarkers in Living Labs for Vulnerable and Susceptible Individuals: An Integrative Literature Review. Yonsei Medical Journal 2022;63(Suppl):S43 View
  5. Makimoto K, Au R, Moslemi A, Hogg J, Bourbeau J, Tan W, Kirby M. Comparison of Feature Selection Methods and Machine Learning Classifiers for Predicting Chronic Obstructive Pulmonary Disease Using Texture-Based CT Lung Radiomic Features. Academic Radiology 2023;30(5):900 View
  6. Park Y, Lee C, Jung J. Digital Healthcare for Airway Diseases from Personal Environmental Exposure. Yonsei Medical Journal 2022;63(Suppl):S1 View
  7. Pinnock H, Murphie P, Vogiatzis I, Poberezhets V. Telemedicine and virtual respiratory care in the era of COVID-19. ERJ Open Research 2022;8(3):00111-2022 View
  8. Alvarado E, Grágeda N, Luzanto A, Mahu R, Wuth J, Mendoza L, Yoma N. Dyspnea Severity Assessment Based on Vocalization Behavior with Deep Learning on the Telephone. Sensors 2023;23(5):2441 View
  9. Watson A, Wilkinson T. Digital healthcare in COPD management: a narrative review on the advantages, pitfalls, and need for further research. Therapeutic Advances in Respiratory Disease 2022;16 View
  10. Guo S, Akman C. Logistic Regression Analysis of Clinical Characteristics for Differentiation of Chronic Obstructive Pulmonary Disease Severity. Emergency Medicine International 2023;2023:1 View
  11. Fu Y, Kang N, Yu Y, Mi Y, Guo J, Wu J, Weng C. Polyphenols, flavonoids and inflammasomes: the role of cigarette smoke in COPD. European Respiratory Review 2022;31(164):220028 View
  12. Takano A, Ono K, Nozawa K, Sato M, Onuki M, Sese J, Yumoto Y, Matsushita S, Matsumoto T. Wearable Sensor and Mobile App–Based mHealth Approach for Investigating Substance Use and Related Factors in Daily Life: Protocol for an Ecological Momentary Assessment Study. JMIR Research Protocols 2023;12:e44275 View
  13. Zhang B, Wang J, Chen J, Ling Z, Ren Y, Xiong D, Guo L. Machine learning in chronic obstructive pulmonary disease. Chinese Medical Journal 2023;136(5):536 View
  14. Pépin J, Degano B, Tamisier R, Viglino D. Remote Monitoring for Prediction and Management of Acute Exacerbations in Chronic Obstructive Pulmonary Disease (AECOPD). Life 2022;12(4):499 View
  15. Neumann D, Tiberius V, Biendarra F. Adopting wearables to customize health insurance contributions: a ranking-type Delphi. BMC Medical Informatics and Decision Making 2022;22(1) View
  16. Xie Y, Lu L, Gao F, He S, Zhao H, Fang Y, Yang J, An Y, Ye Z, Dong Z. Integration of Artificial Intelligence, Blockchain, and Wearable Technology for Chronic Disease Management: A New Paradigm in Smart Healthcare. Current Medical Science 2021;41(6):1123 View
  17. Mozumder M, Armand T, Imtiyaj Uddin S, Athar A, Sumon R, Hussain A, Kim H. Metaverse for Digital Anti-Aging Healthcare: An Overview of Potential Use Cases Based on Artificial Intelligence, Blockchain, IoT Technologies, Its Challenges, and Future Directions. Applied Sciences 2023;13(8):5127 View
  18. Wieben A, Walden R, Alreshidi B, Brown S, Cato K, Coviak C, Cruz C, D'Agostino F, Douthit B, Forbes T, Gao G, Johnson S, Lee M, Mullen-Fortino M, Park J, Park S, Pruinelli L, Reger A, Role J, Sileo M, Schultz M, Vyas P, Jeffery A. Data Science Implementation Trends in Nursing Practice: A Review of the 2021 Literature. Applied Clinical Informatics 2023;14(03):585 View
  19. ZhuParris A, de Goede A, Yocarini I, Kraaij W, Groeneveld G, Doll R. Machine Learning Techniques for Developing Remotely Monitored Central Nervous System Biomarkers Using Wearable Sensors: A Narrative Literature Review. Sensors 2023;23(11):5243 View
  20. Gálvez-Barrón C, Pérez-López C, Villar-Álvarez F, Ribas J, Formiga F, Chivite D, Boixeda R, Iborra C, Rodríguez-Molinero A. Machine learning for the development of diagnostic models of decompensated heart failure or exacerbation of chronic obstructive pulmonary disease. Scientific Reports 2023;13(1) View
  21. Althobiani M, Khan B, Shah A, Ranjan Y, Mendes R, Folarin A, Mandal S, Porter J, Hurst J. Clinicians’ Perspectives of Wearable Technology to Detect and Monitor Exacerbations of Chronic Obstructive Pulmonary Disease: Mixed-Method Survey. International Journal of Chronic Obstructive Pulmonary Disease 2023;Volume 18:1401 View
  22. Zhang J, Chen F, Wang Y, Chen Y. Early detection and prediction of acute exacerbation of chronic obstructive pulmonary disease. Chinese Medical Journal Pulmonary and Critical Care Medicine 2023;1(2):102 View
  23. Wang X, Qiao Y, Cui Y, Ren H, Zhao Y, Linghu L, Ren J, Zhao Z, Chen L, Qiu L. An explainable artificial intelligence framework for risk prediction of COPD in smokers. BMC Public Health 2023;23(1) View
  24. 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
  25. Jacobson P, Lind L, Persson H. The Exacerbation of Chronic Obstructive Pulmonary Disease: Which Symptom is Most Important to Monitor?. International Journal of Chronic Obstructive Pulmonary Disease 2023;Volume 18:1533 View
  26. Coutu F, Iorio O, Ross B. Remote patient monitoring strategies and wearable technology in chronic obstructive pulmonary disease. Frontiers in Medicine 2023;10 View
  27. BAGABIR S. Comparison of pulmonary rehabilitation and chest physiotherapy's outcomes among elder patients with chronic obstructive pulmonary disease: a meta-analysis. Minerva Biotechnology and Biomolecular Research 2023;35(2) View
  28. Sami R, Salehi K, Hashemi M, Haghighat S, Atashi V. Barriers to adherence to home-based pulmonary rehabilitation among patients with chronic obstructive pulmonary disease in Iran: a descriptive qualitative study. BMJ Open 2023;13(10):e073972 View
  29. Liu J, Shih C, Huang H, Peng J, Cheng S, Tsai J, Lai F. Evaluating the Potential of Machine Learning and Wearable Devices in End-of-Life Care in Predicting 7-Day Death Events Among Patients With Terminal Cancer: Cohort Study. Journal of Medical Internet Research 2023;25:e47366 View
  30. Liao X, Wu Y, Jiang N, Sun J, Xu W, Gao S, Wang J, Li T, Wang K, Li Q. Automated detection of abnormal respiratory sound from electronic stethoscope and mobile phone using MobileNetV2. Biocybernetics and Biomedical Engineering 2023;43(4):763 View
  31. Shah A, Althobiani M, Saigal A, Ogbonnaya C, Hurst J, Mandal S. Wearable technology interventions in patients with chronic obstructive pulmonary disease: a systematic review and meta-analysis. npj Digital Medicine 2023;6(1) View
  32. Gálvez-Barrón C, Pérez-López C. Sistemas diagnósticos de exacerbación de la EPOC en la población anciana: presente y futuro. Open Respiratory Archives 2024;6(1):100291 View
  33. Kurian V, Gee M, Farrington S, Yang E, Okossi A, Chen L, Beris A. Systems Engineering Approach to Modeling and Analysis of Chronic Obstructive Pulmonary Disease Part II: Extension for Variable Metabolic Rates. ACS Omega 2024;9(1):494 View
  34. Vitacca M, Malovini A, Paneroni M, Spanevello A, Ceriana P, Capelli A, Murgia R, Ambrosino N. Predicting Response to In-Hospital Pulmonary Rehabilitation in Individuals Recovering From Exacerbations of Chronic Obstructive Pulmonary Disease. Archivos de Bronconeumología 2024;60(3):153 View
  35. 张 欢. Research Progress of Risk Factors andAntibiotic Therapy in Elderly Patients with AECOPD. Asian Case Reports in Emergency Medicine 2024;12(01):8 View
  36. Pan Z, Liao S, Sun W, Zhou H, Lin S, Chen D, Jiang S, Long H, Fan J, Deng F, Zhang W, Chen B, Wang J, Huang Y, Li J, Chen Y. Screening and early warning system for chronic obstructive pulmonary disease with obstructive sleep apnoea based on the medical Internet of Things in three levels of healthcare: protocol for a prospective, multicentre, observational cohort study. BMJ Open 2024;14(2):e075257 View
  37. Dhaliwal M, Sharma R, Kaur R. PIF dataset: a comprehensive dataset of physiological and inertial features for recognition of human activities. Multimedia Tools and Applications 2024;83(29):73607 View
  38. Damaševičius R, Jagatheesaperumal S, Kandala R, Hussain S, Alizadehsani R, Gorriz J. Deep learning for personalized health monitoring and prediction: A review. Computational Intelligence 2024;40(3) View
  39. Shu H, Lin C, He B, Wang W, Wang L, Wu T, He H, Wang H, Zhou H, Ding G. Pyroptosis-Related Genes as Diagnostic Markers in Chronic Obstructive Pulmonary Disease and Its Correlation with Immune Infiltration. International Journal of Chronic Obstructive Pulmonary Disease 2024;Volume 19:1491 View
  40. 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
  41. Ortiz-Barrios M, Ishizaka A, Barbati M, Arias-Fonseca S, Khan J, Gul M, Yücesan M, Alfaro-Saíz J, Pérez-Aguilar A. Integrating discrete-event simulation and artificial intelligence for shortening bed waiting times in hospitalization departments during respiratory disease seasons. Computers & Industrial Engineering 2024;194:110405 View
  42. Hegeman P, Vader D, Kamke K, El-Toukhy S. Patterns of digital health access and use among US adults: a latent class analysis. BMC Digital Health 2024;2(1) View
  43. Amirthalingam P, Alatawi Y, Chellamani N, Shanmuganathan M, Ali M, Alqifari S, Mani V, Dhanasekaran M, Alqahtani A, Alanazi M, Aljabri A. Sea Horse Optimization–Deep Neural Network: A Medication Adherence Monitoring System Based on Hand Gesture Recognition. Sensors 2024;24(16):5224 View
  44. Wiebe M, Mackay M, Krishnan R, Tian J, Larsson J, Modanloo S, Job McIntosh C, Sztym M, Elton-Smith G, Rose A, Ho C, Greenshaw A, Cao B, Chan A, Hayward J, Frasch M. Feasibility characteristics of wrist-worn fitness trackers in health status monitoring for post-COVID patients in remote and rural areas. PLOS Digital Health 2024;3(8):e0000571 View
  45. Kamis A, Gadia N, Luo Z, Ng S, Thumbar M. Obtaining the Most Accurate, Explainable Model for Predicting Chronic Obstructive Pulmonary Disease: Triangulation of Multiple Linear Regression and Machine Learning Methods. JMIR AI 2024;3:e58455 View
  46. Debeij S, Aardoom J, Haaksma M, Stoop W, van Dam van Isselt E, Kasteleyn M. The Potential Use and Value of a Wearable Monitoring Bracelet for Patients With Chronic Obstructive Pulmonary Disease: Qualitative Study Investigating the Patient and Health Care Professional Perspectives. JMIR Formative Research 2024;8:e57108 View
  47. Fatima M, Ahmad A, Butt I, Arshad S, Kiani B. Geospatial modelling of ambient air pollutants and chronic obstructive pulmonary diseases at regional scale in Pakistan. Environmental Monitoring and Assessment 2024;196(10) View
  48. Huang L, Guan Q, Lu R, Zhang Z, Liu C, Tian Y, Li J. Mechanism underlying the therapeutic effects of effective component compatibility of Bufei Yishen formula III combined with exercise rehabilitation on chronic obstructive pulmonary disease. Annals of Medicine 2024;56(1) View
  49. Chen C, Lai F, Huang L, Guo Y. Short- and medium-term cumulative effects of traffic-related air pollution on resting heart rate in the elderly: A wearable device study. Ecotoxicology and Environmental Safety 2024;285:117140 View
  50. Sundas A, Contreras I, Mujahid O, Beneyto A, Vehi J. The Effects of Environmental Factors on General Human Health: A Scoping Review. Healthcare 2024;12(21):2123 View
  51. Graña-Castro O, Izquierdo E, Piñas-Mesa A, Menasalvas E, Chivato-Pérez T. Assessing the Impact of New Technologies on Managing Chronic Respiratory Diseases. Journal of Clinical Medicine 2024;13(22):6913 View
  52. Gómez-López A, Arismendi E, Cano I, Farre R, Figols M, Hernández C, Montilla-Ibarra A, Sánchez-Ruano N, Sánchez B, Sisó-Almirall A, Sorribes M, Vela E, Piera-Jiménez J, Benavent J, Fermoso J, Roca J, González-Colom R. Protocol for the enhanced management of multimorbid patients with COPD and severe asthma: role of indoor air quality. BMJ Open Respiratory Research 2025;12(1):e002589 View
  53. 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
  54. 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
  55. Yang H, Wei C, Zhou S, Mao F. Machine learning-based identification of high-risk bone metastasis factors after radical prostatectomy in prostate cancer. Frontiers in Oncology 2025;15 View
  56. Otapo A, Othmani A, Khodabandelou G, Ming Z. Prediction and detection of terminal diseases using Internet of Medical Things: A review. Computers in Biology and Medicine 2025;188:109835 View
  57. Dong C, Ji Y, Fu Z, Qi Y, Yi T, Yang Y, Sun Y, Sun H. Precision management in chronic disease: An AI empowered perspective on medicine-engineering crossover. iScience 2025;28(3):112044 View
  58. Ji Q, Meng Y, Han X, Yi C, Chen X, Zhan Y. Bioinformatic Insights and XGBoost Identify Shared Genetics in Chronic Obstructive Pulmonary Disease and Type 2 Diabetes. The Clinical Respiratory Journal 2025;19(3) View
  59. 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
  60. Melzer T, Kahnert K. Meteorologische Einflüsse auf Krankenhauseinweisungen bei Asthma und COPD. Pneumo News 2025;17(2):20 View
  61. SUN R, HE L. THE HEALTH MONITORING ANALYSIS OF PATIENTS WITH CHRONIC OBSTRUCTIVE PULMONARY DISEASE USING DEEP LEARNING. Journal of Mechanics in Medicine and Biology 2025;25(05) View
  62. Malyavin A, Chumovatov N, Chernyh N. Tobacco smoking as a factor in the development of COPD and pulmonary tuberculosis. Journal of Respiratory Medicine 2025;1(2):45 View
  63. Paul J, Azmal M, Talukder O, Haque A, Meem M, Ghosh A. Harnessing machine learning for improved diagnosis, drug discovery, and patient care. Computational and Structural Biotechnology Reports 2025;2:100051 View
  64. Meng X, Lang Y, Li X, Li Y, Bu Z, Wu Y, Zhang S. Progress in the application of AI in the standardization of traditional Chinese medicine: A review based on machine learning and deep learning. Pharmacological Research - Modern Chinese Medicine 2025;16:100639 View
  65. Fukuda Y, Fukuda K. Public Attitudes and Predictors of Public Awareness of Personal Digital Health Data Sharing for Research: Cross-Sectional Study in Japan. JMIR Human Factors 2025;12:e64192 View
  66. Chao A, Martignetti L, Groh R, Kist A, Li-Jessen N. An mHealth App and System Architecture for Respiratory Disease Management: Design Principles, Tool Development, and Pilot Usability Study. JMIR Formative Research 2025;9:e73584 View
  67. Yu K, Jiang S, Shi S, Meng X, Chen R, Kan H, Yin P, Zhou M, Tan F, Liu C, Tong X. Association of non-optimum temperature, heatwaves and cold spells with chronic obstructive pulmonary disease mortality: A nationwide time-stratified case-crossover study. Ecotoxicology and Environmental Safety 2025;302:118760 View
  68. 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
  69. 向 竹. Research Progress on Biomarkers of Frequent Acute Exacerbations of Chronic Obstructive Pulmonary Disease. Advances in Clinical Medicine 2025;15(08):833 View
  70. Zhou J, Zhang Q, Zhang M, Aikebaier S, Zhu X, Zhou X. Flexible wearable medical devices: from material innovations and data processing to intelligent healthcare applications. Journal of Advanced Research 2025 View
  71. Elfouly T, Alouani A. A Comprehensive Survey on Wearable Computing for Mental and Physical Health Monitoring. Electronics 2025;14(17):3443 View
  72. Ooko S, Oginga R. Application of machine learning for early detection of chronic diseases in Africa. Journal of Public Health Research 2025;14(3) View
  73. Feng L, Li J, Su J, Tong Z, Liang L. Increased in-hospital mortality and readmission risk associated with cardiovascular and cerebrovascular comorbidities in acute exacerbation of COPD patients. BMJ Open Respiratory Research 2025;12(1):e003110 View
  74. Wu C, Hsieh M, Chen I, Jhao L, Liu D, Wang S, Wu C, Chien Y. Using Wearable Device and Machine Learning to Predict Mood Symptoms in Bipolar Disorder: Development and Usability Study. JMIR Medical Informatics 2025;13:e66277 View
  75. Shen Y, Li Z, Li S, Zhou L, Chen Y, Dong K, Wang H, Wang D, Hu Y, Li J, Li C. Federated learning for early severity prediction in acute pancreatitis: a multi-center study. BMC Gastroenterology 2025;25(1) View
  76. Zhang C, Zhang C, Jin Z, Yu K, Wei S, Zhang M, Jin Z, Liao J, Wang G. The value of cough sound monitoring via an audio-enabled smartwatch for OSA screening in COPD patients: a cross-sectional exploratory study. Frontiers in Medicine 2025;12 View
  77. 杨 静. Research Progress on Remote Management of Elderly COPD Patients via Digital Health Technology. Nursing Science 2025;14(10):1718 View
  78. Chen J, Chen Z, Yang S, Jiang H, Xie C. Development of an interpretable machine learning model for predicting prolonged hospital stay in patients with acute exacerbation of chronic obstructive pulmonary disease: a retrospective cohort study. BMC Medical Informatics and Decision Making 2025;25(1) View
  79. Zhang Y, Zheng S, Wang D, Lin F, Ma Y, Qiang Z, Zhang T, Zhong H, Zhou M, Li Z, Chen P, Feng J, Lu W, Liu Y. Development and validation of a machine learning-based model to predict the risk of hospitalization death in hospitalized patients with AECOPD. Scientific Reports 2025;15(1) View
  80. Zhou C, Restko M, Freije B, Burkes R. Artificial intelligence in chronic obstructive pulmonary disease: recent advances in imaging and physiological monitoring. Current Opinion in Pulmonary Medicine 2025 View
  81. Shah A, Saigal A, Pramono R, Dessi O, Hurst J, Mani A, Rodriguez Villegas E, Mandal S. Heart rate, respiratory rate and airflow variability differences between stable and exacerbating COPD patients. ERJ Open Research 2025;11(6):00234-2025 View
  82. Chen C, Lai F, Chen Y, Guo Y. Effects of Heat Adaptation Behaviors on Resting Heart Rate Response to Summer Temperatures in Older Adults: Wearable Device Panel Study. JMIR mHealth and uHealth 2025;13:e67721 View
  83. 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
  84. Jiang J, Zhou Z, Peng T, Huang Z, Pan X, Wu C. Applications of AI/ML in accelerating the development of pulmonary drug delivery system. Acta Pharmaceutica Sinica B 2025 View
  85. 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

Books/Policy Documents

  1. Dhaliwal M, Sharma R, Bindra N. Machine Learning, Image Processing, Network Security and Data Sciences. View
  2. Cai Y, Li J, Fan L, Jiang J. LISS 2021. View
  3. Latif T, Dieffenderfer J, da Silva R, Lobaton E, Bozkurt A. Encyclopedia of Sensors and Biosensors. View
  4. Kabir R, Syed H, Vinnakota D, Sivasubramanian M, Hitch G, Okello S, Sharon-Shivuli-Isigi , Pulikkottil A, Mahmud I, Dehghani L, Parsa A. Deep Learning in Personalized Healthcare and Decision Support. View
  5. Cano I, Arismendi E, Borrat X. Digital Respiratory Healthcare. View
  6. Tran L, Thi H, Chu D. Advances in Bioinformatics. View
  7. Harrison S, Craig C, Loughran K, Trewartha G. Gait, Balance, and Mobility Analysis. View
  8. Amin B, Ghalem B, Dalila B, Amine D, Madjid S. Proceedings of International Conference on Paradigms of Communication, Computing and Data Analytics. View

Conference Proceedings

  1. Becirovic L, Deumic A, Pokvic L, Badnjevic A. 2021 IEEE 21st International Conference on Bioinformatics and Bioengineering (BIBE). Aritificial Inteligence Challenges in COPD management: a review View
  2. Hsiao C, Chu C, Lee R, Chang J, Tseng C. 2023 IEEE International Conference on Systems, Man, and Cybernetics (SMC). Wearable Devices for Early Warning of Acute Exacerbation in Chronic Obstructive Pulmonary Disease Patients View
  3. Abutalip S, Baikuvekov M, Zanggar D. 2024 IEEE 4th International Conference on Smart Information Systems and Technologies (SIST). A Machine Learning Approach for Detection of Heart Diseases Using Wearable Devices View
  4. M S, T S, K A, Subramaniam K. 2024 9th International Conference on Communication and Electronics Systems (ICCES). An Intelligent Early COPD Prediction Using Machine Learning View
  5. Kaulgud R, Adnan Othman N, Akhmedov A, Christal Mary S, Majid A, Balakumar A. 2025 AI-Driven Smart Healthcare for Society 5.0. Evaluating AI Predictive Models for Enhanced Decision-Making in Cardiovascular and Respiratory Diseases View
  6. Chand S, Jaiswal A, Bisht M. 2024 1st International Conference on Sustainability and Technological Advancements in Engineering Domain (SUSTAINED). Leveraging Machine Learning for Accurate COPD Detection View
  7. Islam M, Rashed M, Podder N, Hossain M, Mondol A. 2025 International Conference on Quantum Photonics, Artificial Intelligence, and Networking (QPAIN). Enhancing COPD Detection with WaveNet: Assessing the Efficacy of Deep Learning Model View