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

Reviews in AI (19) Applications of AI (246)

Published on in Vol 2 (2023)

This is a member publication of The University of Edinburgh, Usher Institute, Edinburgh, United Kingdom

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/46717, first published .
Machine Learning–Based Asthma Attack Prediction Models From Routinely Collected Electronic Health Records: Systematic Scoping Review

Machine Learning–Based Asthma Attack Prediction Models From Routinely Collected Electronic Health Records: Systematic Scoping Review

Machine Learning–Based Asthma Attack Prediction Models From Routinely Collected Electronic Health Records: Systematic Scoping Review

Authors of this article:

Arif Budiarto1, 2 Author Orcid Image ;   Kevin C H Tsang1 Author Orcid Image ;   Andrew M Wilson3, 4 Author Orcid Image ;   Aziz Sheikh1 Author Orcid Image ;   Syed Ahmar Shah1 Author Orcid Image
Article Authors Cited by (14) Tweetations (4) Metrics

Journals

  1. di Biase L, Pecoraro P, Pecoraro G, Shah S, Di Lazzaro V. Machine learning and wearable sensors for automated Parkinson’s disease diagnosis aid: a systematic review. Journal of Neurology 2024;271(10):6452 View
  2. Lisik D, Basna R, Dinh T, Hennig C, Shah S, Wennergren G, Goksör E, Nwaru B. Artificial intelligence in pediatric allergy research. European Journal of Pediatrics 2024;184(1) View
  3. Mahmood S, Hasan R, Hussain S, Adhikari R. An Interpretable and Generalizable Machine Learning Model for Predicting Asthma Outcomes: Integrating AutoML and Explainable AI Techniques. World 2025;6(1):15 View
  4. Dinh T, Wong H, Fournier-Viger P, Lisik D, Ha M, Dam H, Huynh V. Categorical data clustering: 25 years beyond K-modes. Expert Systems with Applications 2025;272:126608 View
  5. Shah S. Enhancing COPD Care for Women: A Predictive Tool for Palliative Needs. Respirology 2025;30(7):547 View
  6. Adamu Aliyu D, Akashah Patah Akhir E, Omar Abdullah Sawad M, Shehu Yalli J, Saidu Y. A Reinforcement Learning Approach to Personalized Asthma Exacerbation Prediction Using Proximal Policy Optimization. IEEE Access 2025;13:103373 View
  7. Aziz F, Malek S, Sooriamoorthy S, Mahamood I, Wen C, Syed Ahmad S, Amir Rudin P, Ali A. Predicting medication wastage using machine learning based on patient beliefs. DIGITAL HEALTH 2025;11 View
  8. Abhadiomhen S, Nzeh R, Ezema M, Abhadionmhen A, Uzo B, Ezugwu A. Asthma exacerbation prediction using shallow and deep learning approaches: A systematic review. Network Modeling Analysis in Health Informatics and Bioinformatics 2025;14(1) View

Books/Policy Documents

  1. Maniar H, Molia H, Popat K. Artificial Intelligence Based Smart and Secured Applications. View
  2. Budiarto A, Sheikh A, Wilson A, Price D, Shah S. Pervasive Computing Technologies for Healthcare. View

Conference Proceedings

  1. Hamza A, Himeur Y, Amira A, Oulefki A. 2024 IEEE 10th International Conference on Big Data Computing Service and Machine Learning Applications (BigDataService). Predicting Asthma Attacks Through AI-Powered Thermal Imaging Analysis of Breathing Patterns View
  2. Liang C, Yogarayan S, Abdul Razak S, Masidayu Sayed Ismail S, Azman A, Raman K. 2024 International Conference on Intelligent Cybernetics Technology & Applications (ICICyTA). IoT-Enabled Asthma Risk Prediction: Advancements and Challenges View
  3. Kumar B, Upadhyay D, Gill K, Devliyal S. 2024 2nd International Conference on Advances in Computation, Communication and Information Technology (ICAICCIT). AI-Enhanced Asthma Management: Random Forests Leading the Way View
  4. Kaur A, Gill K, Chauhan R, Pokhariya H. 2024 International Conference on Integration of Emerging Technologies for the Digital World (ICIETDW). Revolutionizing Asthma Control Using Random Forest Machine Learning Enhancement Technique View