A study of the effectiveness of transfer learning in individualized asthma risk prediction
Author:
Affiliation:
1. Seattle University
2. UAEU, United Arab Emirates
3. Lockheed Martin Space Systems
4. Soonchunhyang University, South Korea
5. Soonchunhyang University Bucheon Hospital, South Korea
Funder
Ministry of Environment
Seattle University
Korean Environmental Industry & Technology Institute
Publisher
ACM
Link
https://dl.acm.org/doi/pdf/10.1145/3412841.3442105
Reference16 articles.
1. Journal of Personalized Medicine 2020 10 Applications of machine learning predictive models in the chronic disease diagnosis
2. Complex asthma risk factor recognition from heterogeneous data streams
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1. Is In-Domain Data Beneficial in Transfer Learning for Landmarks Detection in X-Ray Images?;2024 IEEE International Symposium on Biomedical Imaging (ISBI);2024-05-27
2. Review on Prediction and Detection of Lung and Kidney Disease Using Transfer Learning;Proceedings of the 2nd International Conference on Cognitive and Intelligent Computing;2023
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