The Usefulness of Machine Learning Models to Predict Patient‐Reported Outcome Measures in Chronic Rhinosinusitis With Nasal Polyps
- 1Department of Otorhinolaryngology Upper Airway Inflammation and Tumor Laboratory, the First Affiliated Hospital of Chongqing Medical University Chongqing China
- 2Department of Otorhinolaryngology Xishui People's Hospital Zunyi Guizhou China
Abstract
Background and Objective The treatment available for chronic rhinosinusitis with nasal polyps (CRSwNP) has remained unsatisfactory. Patient‐reported outcome measures (PROMs), capturing patient‐perceived health status and well‐being, are vehicles for measuring and improving the efficacy of care. This study aimed to establish machine learning (ML) models to predict PROMs in CRSwNP patients using minimally invasive and easily acquired clinical data. Methods We collected commonly available clinical predictive data from 437 patients and established four separate ML models in the training set: a least absolute shrinkage and selection operator (LASSO)‐based Logistic regression, a random forest (RF) regression, a gradient‐boosted decision tree (GBDT), and a deep neural network (DNN). In the test and independent external validation sets, the predictive performance of these models was measured by calculating C statistics, expected prediction results, and decision curves. A feature‐ranking analysis was performed using the ML algorithm. We then developed a predictive nomogram using LASSO‐based Logistic regression. Results The models performed well on an independent external validation set, with no statistically significant differences in generalization ability metrics between groups. LASSO regression identified key features of the predictive PROMs. A nomogram was created based on multivariate Logistic regression with LASSO regularization. Conclusions All four ML models demonstrated similar performance in predicting PROMs in CRSwNP patients from which a clinical nomogram was developed. Early prediction of the subjective treatment response is crucial, as it influences clinician decisions and facilitates effective doctor‐patient communication preoperatively; this could lead to more precise and personalized treatment for CRSwNP patients.
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BibTeX
@article{shen2026usefulness,
title = {{The Usefulness of Machine Learning Models to Predict Patient‐Reported Outcome Measures in Chronic Rhinosinusitis With Nasal Polyps}},
author = {Yang Shen and Pan‐Hui Xiong and Bo‐Wen Zheng and Chen‐Xi Li and Jun‐Liang Chen and Yue Gu and Yan‐Han Yang and Tao Lu and Yu‐Cheng Yang},
journal = {World Journal of Otorhinolaryngology-Head and Neck Surgery},
year = {2026},
month = oct,
doi = {10.1002/wjo2.70144},
url = {https://doi.org/10.1002/wjo2.70144},
note = {CC BY},
}
RIS
TY - JOUR
TI - The Usefulness of Machine Learning Models to Predict Patient‐Reported Outcome Measures in Chronic Rhinosinusitis With Nasal Polyps
AU - Yang Shen
AU - Pan‐Hui Xiong
AU - Bo‐Wen Zheng
AU - Chen‐Xi Li
AU - Jun‐Liang Chen
AU - Yue Gu
AU - Yan‐Han Yang
AU - Tao Lu
AU - Yu‐Cheng Yang
PY - 2026
DA - 2026/10/23
JO - World Journal of Otorhinolaryngology-Head and Neck Surgery
DO - 10.1002/wjo2.70144
UR - https://doi.org/10.1002/wjo2.70144
AB - Background and Objective The treatment available for chronic rhinosinusitis with nasal polyps (CRSwNP) has remained unsatisfactory. Patient‐reported outcome measures (PROMs), capturing patient‐perceived health status and well‐being, are vehicles for measuring and improving the efficacy of care. This study aimed to establish machine learning (ML) models to predict PROMs in CRSwNP patients using minimally invasive and easily acquired clinical data. Methods We collected commonly available clinical predictive data from 437 patients and established four separate ML models in the training set: a least absolute shrinkage and selection operator (LASSO)‐based Logistic regression, a random forest (RF) regression, a gradient‐boosted decision tree (GBDT), and a deep neural network (DNN). In the test and independent external validation sets, the predictive performance of these models was measured by calculating C statistics, expected prediction results, and decision curves. A feature‐ranking analysis was performed using the ML algorithm. We then developed a predictive nomogram using LASSO‐based Logistic regression. Results The models performed well on an independent external validation set, with no statistically significant differences in generalization ability metrics between groups. LASSO regression identified key features of the predictive PROMs. A nomogram was created based on multivariate Logistic regression with LASSO regularization. Conclusions All four ML models demonstrated similar performance in predicting PROMs in CRSwNP patients from which a clinical nomogram was developed. Early prediction of the subjective treatment response is crucial, as it influences clinician decisions and facilitates effective doctor‐patient communication preoperatively; this could lead to more precise and personalized treatment for CRSwNP patients.
SN - 2589-1081
SN - 2095-8811
ER -