Re: Letter to the Editor Regarding “Uncovering Key Features for Predicting Comorbid Chronic Eosinophilic Pneumonia in Chronic Rhinosinusitis Via Machine Learning”
The authors declare no competing interests. Data supporting the findings of this study are available upon request from the corresponding author.
Authors
- Hiroatsu Hatsukawa (ORCID: https://orcid.org/0009-0006-5187-1465)
- Tomoyuki Hirai
- Satoru Terada (ORCID: https://orcid.org/0000-0003-1748-5502)
- Zhiqian Jiang (ORCID: https://orcid.org/0000-0002-8036-9937)
- Hirotaka Matsumoto (ORCID: https://orcid.org/0000-0003-4538-6456)
- Hiroshi Mamitsuka (ORCID: https://orcid.org/0000-0002-6607-5617)
- Canh Hao Nguyen (ORCID: https://orcid.org/0000-0002-0274-5693)
- Emiko Saito
- Masaaki Ishikawa (ORCID: https://orcid.org/0000-0002-3638-5366)
- Kouya Okazaki
- Kazuo Endo
Institutions
- Kyoto University (JP)
- Kyoto Bunkyo University (JP)
- Hyogo Prefectural Amagasaki General Medical Center (JP)
- Kyoto University Institute for Chemical Research
Publication Details
- Journal
- International Forum of Allergy & Rhinology
- Published
- 2025-11-08
- DOI
- https://doi.org/10.1002/alr.70062
- Primary Topic
- Sinusitis and nasal conditions
- Type
- article
- Field-Weighted Citation Impact
- 0.00
Funders
- Japanese Society of Allergology
- Ministry of Education, Culture, Sports, Science and Technology
- Japan Society for the Promotion of Science