{
  "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.\n\nObjective: 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.\n\nMethods: 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.\n\nResults: 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.\n\nConclusion: 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.",
  "abstract_shown": true,
  "affiliations": [
    "School of Nursing, Capital Medical University",
    "Beijing Anzhen Hospital, Capital Medical University"
  ],
  "article_type": null,
  "arxiv": null,
  "author_total": 3,
  "authors": [
    {
      "affs": [
        0,
        1
      ],
      "name": "Miao Yu"
    },
    {
      "affs": [
        0
      ],
      "name": "Fangyu Yang"
    },
    {
      "affs": [
        1
      ],
      "name": "Xiaoxia Yu"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/3mhb9henap75j.bib",
  "cited_by": 0,
  "doi": "10.3389/fpubh.2026.2003468",
  "fulltext_url": "https://www.frontiersin.org/articles/10.3389/fpubh.2026.2003468/full",
  "id": "3mhb9henap75j",
  "issns": [
    "2296-2565"
  ],
  "landing_url": "https://www.frontiersin.org/articles/10.3389/fpubh.2026.2003468/full",
  "language": "en",
  "licence": "CC-BY-4.0",
  "licence_label": "CC BY 4.0",
  "licence_source": "article",
  "licence_url": "https://creativecommons.org/licenses/by/4.0/",
  "metadata_licence": null,
  "native_subjects": [
    [
      "lcc",
      "RA1-1270"
    ],
    [
      "openalex",
      "3605|Artificial Intelligence in Healthcare"
    ]
  ],
  "openalex": "W7223410058",
  "pdf_url": "https://www.frontiersin.org/journals/public-health/articles/10.3389/fpubh.2026.2003468/pdf",
  "pmcid": null,
  "pmid": null,
  "published": "2026-10-23",
  "publisher": "Frontiers Media SA",
  "ris": "https://research.draconitic.com/paper/3mhb9henap75j.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:20:24Z",
      "native_id": "10.3389/fpubh.2026.2003468",
      "source": "crossref",
      "url": "https://www.frontiersin.org/articles/10.3389/fpubh.2026.2003468/full"
    },
    {
      "first_seen": "2026-10-30T13:43:12Z",
      "native_id": "c03e0def90ea402a926a9c3d415164f2",
      "source": "doaj",
      "url": "https://doi.org/10.3389/fpubh.2026.2003468"
    }
  ],
  "subjects": [
    {
      "field": "medicine",
      "origin": "lcc:RA1-1270",
      "subfield": "epidemiology-public-health"
    }
  ],
  "title": "Development and temporal validation of an explainable machine-learning model for identifying anxiety symptoms in adults with sudden sensorineural hearing loss",
  "title_text": "Development and temporal validation of an explainable machine-learning model for identifying anxiety symptoms in adults with sudden sensorineural hearing loss",
  "type": "peer-reviewed",
  "updated_at": "2026-10-30T13:43:12Z",
  "url": "https://research.draconitic.com/paper/3mhb9henap75j",
  "venue": "Frontiers in Public Health",
  "versions": []
}