Predicting Surgical Outcomes in Chronic Rhinosinusitis From Preoperative Patient Data: A Machine Learning Approach
The Ensemble MLM achieved the highest overall accuracy and discriminative performance and matched LR for the highest specificity, suggesting that this model could be used in clinical decision-making to "rule in" patients likely to achieve MCID after ESS. This is the highest performance achieved in an MLM trained toward clinical outcomes in CRS. After validation in a multicenter cohort, the Ensemble MLM could become a useful adjunct in clinical decision-making for ESS.
Authors
- Mohamed Aboueisha (ORCID: https://orcid.org/0000-0003-2747-5009)
- Aria Jafari (ORCID: https://orcid.org/0000-0002-5254-3221)
- Ian M. Humphreys (ORCID: https://orcid.org/0000-0002-6832-754X)
- Waleed M. Abuzeid (ORCID: https://orcid.org/0000-0003-2489-0514)
- Arun M. Raghavan (ORCID: https://orcid.org/0000-0001-5339-3352)
- John Paul Giliberto (ORCID: https://orcid.org/0000-0001-8706-3909)
- E. Helene Sage (ORCID: https://orcid.org/0009-0001-0404-5623)
- Ion Prohnitchi
- Mahdi Al‐Ghezi
Institutions
- University of Washington (US)
Publication Details
- Journal
- International Forum of Allergy & Rhinology
- Published
- 2025-11-20
- DOI
- https://doi.org/10.1002/alr.70068
- Primary Topic
- Sinusitis and nasal conditions
- Type
- article
- Field-Weighted Citation Impact
- 0.00