Suppr超能文献

基于问卷回答和社会人口统计学数据的耳鸣患者抑郁严重程度预测模型的开发和内部验证。

Development and internal validation of a depression severity prediction model for tinnitus patients based on questionnaire responses and socio-demographics.

机构信息

Faculty of Computer Science, Otto von Guericke University Magdeburg, Universitätsplatz 2, Magdeburg, 39106, Germany.

Tinnitus Center, Charité Universitaetsmedizin Berlin, Charitéplatz 1, Berlin, 10117, Germany.

出版信息

Sci Rep. 2020 Mar 13;10(1):4664. doi: 10.1038/s41598-020-61593-z.

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.

摘要

耳鸣是一种复杂的病症,与重大的心理和经济障碍有关——部分原因是存在各种共病,如抑郁症。因此,了解耳鸣与抑郁症之间的相互作用可能会改善这两个症状群的预防、诊断和治疗。在这项研究中,我们开发并验证了一个机器学习模型,以根据治疗前(T0)获得的变量预测门诊治疗(T1)后抑郁症的严重程度。共有 1490 名慢性耳鸣患者(合并有重度抑郁症:52.2%)完成了为期 7 天的多模态治疗,包括耳鸣特异性成分、认知行为疗法、物理疗法和信息咨询。从 T0 获得的自我报告问卷和社会人口统计学数据中提取了 185 个变量。我们使用了 11 种分类方法来训练模型,这些模型可以可靠地区分 T1 时使用一般抑郁问卷测量的亚临床和临床抑郁症。为了确保高度预测性和稳健的分类器,我们在 10 倍交叉验证方案中调整了算法超参数。为了降低模型复杂性并提高可解释性,我们将模型训练包装在一个增量特征选择机制中,该机制保留了有助于模型预测的特征。我们确定了一个包含所有 185 个特征的 LASSO 模型,以获得最高的预测性能(AUC = 0.87±0.04)。通过我们的特征选择包装器,我们确定了一个具有良好预测性能和可解释性之间折衷的 LASSO 模型,该模型仅使用 6 个特征(AUC = 0.85±0.05)。因此,预测性机器学习模型可以帮助更好地理解耳鸣患者的抑郁症,并有助于为伴有或不伴有合并重度抑郁症的慢性耳鸣患者选择合适的治疗策略和简洁有效的问卷设计。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/26cc/7069984/44301c882f8f/41598_2020_61593_Fig1_HTML.jpg

相似文献

2
Tinnitus-related distress after multimodal treatment can be characterized using a key subset of baseline variables.
PLoS One. 2020 Jan 30;15(1):e0228037. doi: 10.1371/journal.pone.0228037. eCollection 2020.
3
Gender-Specific Differences in Patients With Chronic Tinnitus-Baseline Characteristics and Treatment Effects.
Front Neurosci. 2020 May 25;14:487. doi: 10.3389/fnins.2020.00487. eCollection 2020.
4
Tinnitus severity is reduced with reduction of depressive mood--a prospective population study in Sweden.
PLoS One. 2012;7(5):e37733. doi: 10.1371/journal.pone.0037733. Epub 2012 May 22.
6
[Psychometric instruments for the diagnosis of tinnitus].
HNO. 2012 Aug;60(8):732-42. doi: 10.1007/s00106-011-2403-z.
7
Socio-demographic, health, and tinnitus related variables affecting tinnitus severity.
Ear Hear. 2014 Sep-Oct;35(5):544-54. doi: 10.1097/AUD.0000000000000045.
9
Tinnitus severity and the relation to depressive symptoms: a critical study.
Otolaryngol Head Neck Surg. 2011 Aug;145(2):276-81. doi: 10.1177/0194599811403381.
10
Tinnitus, depression, and suicidal ideation in adults: A nationally representative general population sample.
J Psychiatr Res. 2018 Mar;98:124-132. doi: 10.1016/j.jpsychires.2018.01.003. Epub 2018 Jan 10.

引用本文的文献

2
[Patient-reported outcome measures-use in diagnosing depression, anxiety, and stress].
HNO. 2025 Mar;73(3):196-202. doi: 10.1007/s00106-024-01530-y. Epub 2024 Nov 25.
5
Tinnitus and Influencing Comorbidities.
Laryngorhinootologie. 2023 May;102(S 01):S50-S58. doi: 10.1055/a-1950-6149. Epub 2023 May 2.
6
Dimensions of Tinnitus-Related Distress.
Brain Sci. 2022 Feb 16;12(2):275. doi: 10.3390/brainsci12020275.
7
A State-of-Art Review of Digital Technologies for the Next Generation of Tinnitus Therapeutics.
Front Digit Health. 2021 Aug 10;3:724370. doi: 10.3389/fdgth.2021.724370. eCollection 2021.
8
Taiwanese Depression Questionnaire and AD8 Questionnaire for Screening Late-Life Depression in Communities.
Neuropsychiatr Dis Treat. 2021 Mar 9;17:747-755. doi: 10.2147/NDT.S298233. eCollection 2021.
9
A Review and a Framework of Variables for Defining and Characterizing Tinnitus Subphenotypes.
Brain Sci. 2020 Dec 4;10(12):938. doi: 10.3390/brainsci10120938.

本文引用的文献

文献AI研究员

20分钟写一篇综述,助力文献阅读效率提升50倍。

立即体验

用中文搜PubMed

大模型驱动的PubMed中文搜索引擎

马上搜索

文档翻译

学术文献翻译模型,支持多种主流文档格式。

立即体验