Development and temporal validation of an explainable machine-learning model for identifying anxiety symptoms in adults with sudden sensorineural hearing loss
- 1School of Nursing, Capital Medical University
- 2Beijing Anzhen Hospital, Capital Medical University
Abstract
Background: Sudden sensorineural hearing loss (SSNHL) may be accompanied by substantial psychological distress, yet machine-learning studies have focused mainly on hearing recovery rather than anxiety during acute care.
Objective: To develop, internally validate, temporally evaluate, and interpret machine-learning models for identifying anxiety symptoms in adults hospitalized with first-episode SSNHL after initial treatment and before discharge.
Methods: This single-center retrospective prediction-model study included 512 patients treated at Beijing Anzhen Hospital from February 2022 to August 2023. The 410-patient development cohort was split once, using seed 24, into training ( n = 287) and internal model-selection ( n = 123) datasets; 102 later patients formed a same-center temporal-validation cohort. Anxiety symptoms were operationalized as Hamilton Anxiety Rating Scale (HAM-A) score ≥7. Fourteen candidate predictors entered LASSO, Boruta, and recursive feature elimination, and eight classifiers were compared. Robustness analyses included full-pipeline repeated nested cross-validation using all 410 development observations, overlapping-symptom exclusion models, logistic comparators, corrected calibration, a development-only transported threshold, HAM-A ≥ 8 sensitivity, and continuous HAM-A severity. The temporal cohort remained excluded from feature selection, preprocessing, tuning, and algorithm selection.
Results: Overall, 320/512 patients (62.5%) met the HAM-A ≥ 7 criterion. Six predictors were retained in the primary analysis: hearing-loss severity, tinnitus, vertigo, sleep status, number of comorbidities, and treatment efficacy. The selected SVM achieved ROC-AUCs of 0.772 internally and 0.763 temporally. In nested cross-validation, pooled ROC-AUC was 0.747 (95% CI 0.696–0.792), PR-AUC 0.822 (0.768–0.867), and Brier score 0.208 (0.196–0.221); calibration-in-the-large was 0.451 and calibration slope 1.570. XGBoost was selected in 8/10 outer evaluations and RF in 2/10; SVM was not selected. Four predictors—hearing-loss severity, sleep status, number of comorbidities, and treatment efficacy—were retained in all outer evaluations. Using HAM-A ≥ 8 reduced prevalence to 57.0% and temporal SVM ROC-AUC from 0.759 to 0.730.
Conclusion: The SVM selected in the primary workflow showed moderate same-center temporal discrimination, but nested validation demonstrated model-selection instability and imperfect calibration. Together with HAM-A threshold and symptom-overlap sensitivity analyses, these findings support an investigational model-development framework rather than superiority of a specific SVM or an implementation-ready rule. Geographic multicenter validation, a prospectively locked model and threshold, and prospective impact assessment are required.
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BibTeX
@article{yu2026development,
title = {{Development and temporal validation of an explainable machine-learning model for identifying anxiety symptoms in adults with sudden sensorineural hearing loss}},
author = {Miao Yu and Fangyu Yang and Xiaoxia Yu},
journal = {Frontiers in Public Health},
year = {2026},
month = oct,
doi = {10.3389/fpubh.2026.2003468},
url = {https://www.frontiersin.org/articles/10.3389/fpubh.2026.2003468/full},
note = {CC BY 4.0},
}
RIS
TY - JOUR
TI - Development and temporal validation of an explainable machine-learning model for identifying anxiety symptoms in adults with sudden sensorineural hearing loss
AU - Miao Yu
AU - Fangyu Yang
AU - Xiaoxia Yu
PY - 2026
DA - 2026/10/23
JO - Frontiers in Public Health
DO - 10.3389/fpubh.2026.2003468
UR - https://www.frontiersin.org/articles/10.3389/fpubh.2026.2003468/full
AB - Background: Sudden sensorineural hearing loss (SSNHL) may be accompanied by substantial psychological distress, yet machine-learning studies have focused mainly on hearing recovery rather than anxiety during acute care. Objective: To develop, internally validate, temporally evaluate, and interpret machine-learning models for identifying anxiety symptoms in adults hospitalized with first-episode SSNHL after initial treatment and before discharge. Methods: This single-center retrospective prediction-model study included 512 patients treated at Beijing Anzhen Hospital from February 2022 to August 2023. The 410-patient development cohort was split once, using seed 24, into training ( n = 287) and internal model-selection ( n = 123) datasets; 102 later patients formed a same-center temporal-validation cohort. Anxiety symptoms were operationalized as Hamilton Anxiety Rating Scale (HAM-A) score ≥7. Fourteen candidate predictors entered LASSO, Boruta, and recursive feature elimination, and eight classifiers were compared. Robustness analyses included full-pipeline repeated nested cross-validation using all 410 development observations, overlapping-symptom exclusion models, logistic comparators, corrected calibration, a development-only transported threshold, HAM-A ≥ 8 sensitivity, and continuous HAM-A severity. The temporal cohort remained excluded from feature selection, preprocessing, tuning, and algorithm selection. Results: Overall, 320/512 patients (62.5%) met the HAM-A ≥ 7 criterion. Six predictors were retained in the primary analysis: hearing-loss severity, tinnitus, vertigo, sleep status, number of comorbidities, and treatment efficacy. The selected SVM achieved ROC-AUCs of 0.772 internally and 0.763 temporally. In nested cross-validation, pooled ROC-AUC was 0.747 (95% CI 0.696–0.792), PR-AUC 0.822 (0.768–0.867), and Brier score 0.208 (0.196–0.221); calibration-in-the-large was 0.451 and calibration slope 1.570. XGBoost was selected in 8/10 outer evaluations and RF in 2/10; SVM was not selected. Four predictors—hearing-loss severity, sleep status, number of comorbidities, and treatment efficacy—were retained in all outer evaluations. Using HAM-A ≥ 8 reduced prevalence to 57.0% and temporal SVM ROC-AUC from 0.759 to 0.730. Conclusion: The SVM selected in the primary workflow showed moderate same-center temporal discrimination, but nested validation demonstrated model-selection instability and imperfect calibration. Together with HAM-A threshold and symptom-overlap sensitivity analyses, these findings support an investigational model-development framework rather than superiority of a specific SVM or an implementation-ready rule. Geographic multicenter validation, a prospectively locked model and threshold, and prospective impact assessment are required.
SN - 2296-2565
ER -