Predicting the Outcomes of Internet-Based Cognitive Behavioral Therapy for Tinnitus: Applications of Artificial Neural Network and Support Vector Machine.
Purpose: Internet-based cognitive behavioral therapy (ICBT) has been found to be effective for tinnitus management, although there is limited understanding about who will benefit the most from ICBT. Traditional statistical models have largely failed to identify the nonlinear associations and hence f...
| Publicado en: | American Journal of Audiology Vol. 31; pp. 1167 - 1178 |
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| Autores principales: | , , , |
| Formato: | research tables/charts Journal Article |
| Publicado: |
American Speech-Language-Hearing Association
Dec2022
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=160628911&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 160628911 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 10590889 5KS jtl: American Journal of Audiology issn: 10590889 maglogo: N pubinfo: dt: Dec2022 vid: 31 pid: 42 pub: American Speech-Language-Hearing Association place: Rockville, Maryland artinfo: ui: 160628911 160628911 160628911 10.1044/2022_AJA-21-00270 160628911 ppf: 1167 ppct: 11 formats: fmt: @attributes: type: P tig: atl: Predicting the Outcomes of Internet-Based Cognitive Behavioral Therapy for Tinnitus: Applications of Artificial Neural Network and Support Vector Machine. aug: au: Rodrigo, Hansapani Beukes, Eldré W. Andersson, Gerhard Manchaiah, Vinaya affil: School of Mathematical and Statistical Sciences, University of Texas Rio Grande Valley, Edinburg sug: subj: Predictive Value of Tests Internet-Based Intervention Cognitive Therapy Tinnitus Therapy Neural Networks (Computer) Utilization Support Vector Machine Utilization Funding Source Human Male Female Adolescence Adult Middle Age Aged Sensitivity and Specificity Secondary Analysis Scales Questionnaires T-Tests Chi Square Test Fisher's Exact Test Odds Ratio Two-Tailed Test Data Analysis Software Confidence Intervals Descriptive Statistics ROC Curve Adolescent: 13-18 years Adult: 19-44 years Middle Aged: 45-64 years Aged: 65+ years Male Female ab: Purpose: Internet-based cognitive behavioral therapy (ICBT) has been found to be effective for tinnitus management, although there is limited understanding about who will benefit the most from ICBT. Traditional statistical models have largely failed to identify the nonlinear associations and hence find strong predictors of success with ICBT. This study aimed at examining the use of an artificial neural network (ANN) and support vector machine (SVM) to identify variables associated with treatment success in ICBT for tinnitus. Method: The study involved a secondary analysis of data from 228 individuals who had completed ICBT in previous intervention studies. A 13-point reduction in Tinnitus Functional Index (TFI) was defined as a successful outcome. There were 33 predictor variables, including demographic, tinnitus, hearing-related and treatment-related variables, and clinical factors (anxiety, depression, insomnia, hyperacusis, hearing disability, cognitive function, and life satisfaction). Predictive models using ANN and SVM were developed and evaluated for classification accuracy. SHapley Additive exPlanations (SHAP) analysis was used to identify the relative predictor variable importance using the best predictive model for a successful treatment outcome. Results: The best predictive model was achieved with the ANN with an average area under the receiver operating characteristic value of 0.73 ± 0.03. The SHAP analysis revealed that having a higher education level and a greater baseline tinnitus severity were the most critical factors that influence treatment outcome positively. Conclusions: Predictive models such as ANN and SVM help predict ICBT treatment outcomes and identify predictors of outcome. However, further work is needed to examine predictors that were not considered in this study as well as to improve the predictive power of these models. pubtype: Academic Journal doctype: research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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