ArticleScientific reports2020
Development and internal validation of a depression severity prediction model for tinnitus patients based on questionnaire responses and socio-demographics.
Article in Scientific reports, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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Who cites it
10 citing papers in PubMed.
- Article
- Review
- Explainable AI Method for Tinnitus Diagnosis via Neighbor-Augmented Knowledge Graph and Traditional Chinese Medicine: Development and Validation Study.JMIR medical informatics · 2024Article
- Analysis of clinical characteristics and predictive model for effective treatment of tinnitus in patients with transient compound sound therapy.Frontiers in neurology · 2024Article
- Tinnitus and Influencing Comorbidities.Laryngo- rhino- otologie · 2023Review
- Dimensions of Tinnitus-Related Distress.Brain sciences · 2022Article
- A State-of-Art Review of Digital Technologies for the Next Generation of Tinnitus Therapeutics.Frontiers in digital health · 2021Review
- Taiwanese Depression Questionnaire and AD8 Questionnaire for Screening Late-Life Depression in Communities.Neuropsychiatric disease and treatment · 2021Article
- A Review and a Framework of Variables for Defining and Characterizing Tinnitus Subphenotypes.Brain sciences · 2020Review
- Correlation between the Degree of Hearing Loss and the Levels of Anxiety and Depression in Patients with Tinnitus.Noise & healthArticle
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5 authors.
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Abstract
Tinnitus is a complex condition that is associated with major psychological and economic impairments - partly through various comorbidities such as depression. Understanding the interaction between tinnitus and depression may thus improve either symptom cluster's prevention, diagnosis and treatment. In this study, we developed and validated a machine learning model to predict depression severity after outpatient therapy (T1) based on variables obtained before therapy (T0). 1,490 patients with chronic tinnitus (comorbid major depressive disorder: 52.2%) who completed a 7-day multimodal treatment encompassing tinnitus-specific components, cognitive behavioural therapy, physiotherapy and informational counselling were included. 185 variables were extracted from self-report questionnaires and socio-demographic data acquired at T0. We used 11 classification methods to train models that reliably separate between subclinical and clinical depression at T1 as measured by the general depression questionnaire. To ensure highly predictive and robust classifiers, we tuned algorithm hyperparameters in a 10-fold cross-validation scheme. To reduce model complexity and improve interpretability, we wrapped model training around an incremental feature selection mechanism that retained features that contributed to model prediction. We identified a LASSO model that included all 185 features to yield highest predictive performance (AUC = 0.87 ± 0.04). Through our feature selection wrapper, we identified a LASSO model with good trade-off between predictive performance and interpretability that used only 6 features (AUC = 0.85 ± 0.05). Thus, predictive machine learning models can lead to a better understanding of depression in tinnitus patients, and contribute to the selection of suitable therapeutic strategies and concise and valid questionnaire design for patients with chronic tinnitus with or without comorbid major depressive disorder.
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