Please use this identifier to cite or link to this item: http://dx.doi.org/10.25673/36146
Title: Development and internal validation of a depression severity prediction model for tinnitus patients based on questionnaire responses and socio-demographics
Author(s): Niemann, Uli
Brueggemann, Petra
Boecking, Benjamin
Mazurek, Birgit
Spiliopoulou, Myra
Issue Date: 2020
Type: Article
Language: English
URN: urn:nbn:de:gbv:ma9:1-1981185920-363795
Subjects: Prediction model
Tinnitus
Questionnaire responses
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.
URI: https://opendata.uni-halle.de//handle/1981185920/36379
http://dx.doi.org/10.25673/36146
Open Access: Open access publication
License: (CC BY 4.0) Creative Commons Attribution 4.0(CC BY 4.0) Creative Commons Attribution 4.0
Sponsor/Funder: DFG-Publikationsfonds 2020
Journal Title: Scientific reports
Publisher: Macmillan Publishers Limited, part of Springer Nature
Publisher Place: [London]
Volume: 10
Issue: 2020
Original Publication: 10.1038/s41598-020-61593-z
Page Start: 1
Page End: 9
Appears in Collections:Fakultät für Informatik (OA)

Files in This Item:
File Description SizeFormat 
Niemann et al._Development_2020.pdfZweitveröffentlichung13.55 MBAdobe PDFThumbnail
View/Open