DiaTrend：来自先进糖尿病技术的数据集

数据集获取

源站：https://synapse.org/Synapse:syn38187184

标准获取流程：

1. 访问源站链接，注册/登录账号；
2. 进入数据集详情页，按页面指引申请权限或直接下载；
3. 部分平台提供 SDK，例如：
   • OpenDataLab: pip install openxlab 后 openxlab.dataset.get(...)
   • ModelScope: pip install modelscope 后 modelscope download
   • 天池: 登录后页面提供直链下载
     以源站「数据下载/使用文档」页面为准。

数据说明

DiaTrend: A dataset from advanced diabetes technology to enable development of novel analytic solutions ----- ### Principal Investigator Temiloluwa Prioleau, Ph.D. ### Overview Advanced technologies like continuous glucose monitors (CGMs) and insulin pumps are transforming the standard of care for diabetes management. Yet, digital data from these devices is significantly underutilized to optimize outcomes. To advance research and development of robust analytic solutions for the growing population of people with diabetes, there is critical need for open datasets to understand outpatient management, develop interventions, and build robust decision-support tools. This data is released by the Augmented Health Lab (https://ah-lab.t-prioleau.com/) to support and facilitate data-driven research for people living with diabetes. The DiaTrend dataset is composed of intensive longitudinal data from wearable medical devices, including a total of 27,561 days of continuous glucose monitor data and 8,220 days of insulin pump data from 54 patients with diabetes, primarily with type 1 diabetes. The below figure shows an overview of the data collection process and data provided in the DiaTrend dataset. ${image?fileName=Fig1%5FDiaTrend Dataset%5FOverview Fig%2Ejpg&align=Center&scale=60&responsive=true&altText=Overview of the data collection process and data provided in the DiaTrend dataset} Reference code for technical validation on the DiaTrend dataset can be found on our github website here: https://github.com/Augmented-Health-Lab/DiaTrend. ### Data Description Citation Prioleau T., Bartolome A., Comi R., Stanger C. “DiaTrend: A dataset from advanced diabetes technology to enable development of novel analytic solutions,” Scientific Data, 10 (556), 2023. https://doi.org/10.1038/s41597-023-02469-5. [dataset] ### Other Related Publications 1. Cui, Y., Stanger, C. & Prioleau, T. Seasonal, weekly, and individual variations in long-term use of wea

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。