通过大数据可解释人工智能技术刻画助听器用户特征的表3（DOCX）

数据集获取（Figshare）

• 源站标题：Table_3_Profiling hearing aid users through big data explainable artificial intelligence techniques.DOCX
• DOI：10.3389/fneur.2022.933940.s003
• 发布日期：2022-08-26
• 文件数：1 个
• 文件总体积：681.09 KB
• 源站页面：https://frontiersin.figshare.com/articles/dataset/Table_3_Profiling_hearing_aid_users_through_big_data_explainable_artificial_intelligence_techniques_DOCX/20650062

命令行下载

curl -L -o "Table_3_Profiling hearing aid users through big data explainable artificial intelligence techniques.DOCX" "https://ndownloader.figshare.com/files/36857799"

文件清单

• Table_3_Profiling hearing aid users through big data explainable artificial intelligence techniques.DOCX（681.09 KB）

文件总体积 681.09 KB（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

Debilitating hearing loss (HL) affects ~6% of the human population. Only 20% of the people in need of a hearing assistive device will eventually seek and acquire one. The number of people that are satisfied with their Hearing Aids (HAids) and continue using them in the long term is even lower. Understanding the personal, behavioral, environmental, or other factors that correlate with the optimal HAid fitting and with users’’ experience of HAids is a significant step in improving patient satisfaction and quality of life, while reducing societal and financial burden. In SMART BEAR we are addressing this need by making use of the capacity of modern HAids to provide dynamic logging of their operation and by combining this information with a big amount of information about the medical, environmental, and social context of each HAid user. We are studying hearing rehabilitation through a 12-month continuous monitoring of HL patients, collecting data, such as participants’’ demographics, audiometric and medical data, their cognitive and mental status, their habits, and preferences, through a set of medical devices and wearables, as well as through face-to-face and remote clinical assessments and fitting/fine-tuning sessions. Descriptive, AI-based analysis and assessment of the relationships between heterogeneous data and HL-related parameters will help clinical researchers to better understand the overall health profiles of HL patients, and to identify patterns or relations that may be proven essential for future clinical trials. In addition, the future state and behavioral (e.g., HAids Satisfiability and HAids usage) of the patients will be predicted with time-dependent machine learning models to assist the clinical researchers to decide on the nature of the interventions. Explainable Artificial Intelligence (XAI) techniques will be leveraged to better understand the factors that play a significant role in the success of a hearing rehabilitation program, constructing patient profiles. This paper is a conceptual one aiming to describe the upcoming data collection process and proposed framework for providing a comprehensive profile for patients with HL in the context of EU

许可

CC BY 4.0

引用

学术使用请引用 DOI 10.3389/fneur.2022.933940.s003（source: https://frontiersin.figshare.com/articles/dataset/Table_3_Profiling_hearing_aid_users_through_big_data_explainable_artificial_intelligence_techniques_DOCX/20650062）。