A machine learning approach to predicting postoperative recurrence in pediatric chronic rhinosinusitis: identification of key metabolic biomarkers
In this study, we successfully constructed a model to predict the postoperative recurrence of pediatric CRS. The predictive model indicated that key metabolites were significantly associated with disease outcomes, and individualized management of postoperative pediatric CRS.
Authors
- Shenghao Cheng (ORCID: https://orcid.org/0000-0001-9497-6319)
- Sijie Jiang (ORCID: https://orcid.org/0000-0002-6455-3742)
- Benjian Zhang (ORCID: https://orcid.org/0000-0001-5661-8561)
- Weihong Jiang (ORCID: https://orcid.org/0000-0002-3854-8923)
- Shaobing Xie (ORCID: https://orcid.org/0000-0001-8351-651X)
- Junyi Zhang (ORCID: https://orcid.org/0000-0002-3267-542X)
- Hua Zhang (ORCID: https://orcid.org/0000-0003-1809-049X)
- Zhihai Xie
Institutions
- Central South University (CN)
- Xiangya Hospital Central South University (CN)
Publication Details
- Journal
- American Journal of Otolaryngology
- Published
- 2025-05-14
- DOI
- https://doi.org/10.1016/j.amjoto.2025.104676
- Primary Topic
- Sinusitis and nasal conditions
- Type
- article
- Field-Weighted Citation Impact
- 0.00
Funders
- National Natural Science Foundation of China
- Natural Science Foundation of Hunan Province