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
.

Journals
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- 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
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- 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
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Books/Policy Documents
Conference Proceedings
- 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
- 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
- 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
- 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