基于ANN和SVM预测ICBT结果（Rodrigo等，2022）

数据集获取（Figshare）

• 源站标题：Predicting the outcomes of ICBT with ANN and SVM (Rodrigo et al., 2022)
• DOI：10.23641/asha.21266487.v1
• 发布日期：2022-10-10
• 文件数：5 个
• 文件总体积：2.44 MB
• 源站页面：https://asha.figshare.com/articles/dataset/Predicting_the_outcomes_of_ICBT_with_ANN_and_SVM_Rodrigo_et_al_2022_/21266487

命令行下载

curl -L -o "S1_AJA-21-0270rodrigo.pdf" "https://ndownloader.figshare.com/files/37700862"
curl -L -o "S2_AJA-21-0270rodrigo.pdf" "https://ndownloader.figshare.com/files/37700853"
curl -L -o "S3_AJA-21-0270rodrigo.pdf" "https://ndownloader.figshare.com/files/37700856"
curl -L -o "S4_AJA-21-0270rodrigo.pdf" "https://ndownloader.figshare.com/files/37700859"
curl -L -o "S5_AJA-21-0270rodrigo.pdf" "https://ndownloader.figshare.com/files/37700850"

文件清单

• S1_AJA-21-0270rodrigo.pdf（540.41 KB）
• S2_AJA-21-0270rodrigo.pdf（434.31 KB）
• S3_AJA-21-0270rodrigo.pdf（515.36 KB）
• S4_AJA-21-0270rodrigo.pdf（425.63 KB）
• S5_AJA-21-0270rodrigo.pdf（522.04 KB）

文件总体积 2.44 MB（源站 API 实测）。

数据加载示例（图像类）

from PIL import Image
import glob, os

files = (glob.glob(os.path.join(path, "**", "*.png"), recursive=True)
       + glob.glob(os.path.join(path, "**", "*.jpg"), recursive=True)
       + glob.glob(os.path.join(path, "**", "*.tif"), recursive=True))
print("图像文件数:", len(files))
img = Image.open(files[0]); print("尺寸/模式:", img.size, img.mode)

# torchvision Dataset 方式:
# from torchvision import datasets
# ds = datasets.ImageFolder(path)  # 要求 子目录=类别

目录组织与标注格式以源站说明和下载后实际文件为准。

数据说明

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.

Supplemental Material S1. Characteristics of the study 740 participants and summary statistics for successful and unsuccessful treatment groups. Quantitative variables have been analyzed using a two-sample t test, while categorical data have been analyzed with chi-square or Fisher’s exact (denoted by an asterisk

许可

CC BY 4.0

引用

学术使用请引用 DOI 10.23641/asha.21266487.v1（source: https://asha.figshare.com/articles/dataset/Predicting_the_outcomes_of_ICBT_with_ANN_and_SVM_Rodrigo_et_al_2022_/21266487）。