糖尿病前期进展模型验证数据表

数据集获取（Figshare）

• 源站标题：Data Sheet 1_External validation and application of a machine learning–based model for diabetes progression in prediabetes.docx
• DOI：10.3389/fendo.2026.1746570.s001
• 发布日期：2026-03-23
• 文件数：1 个
• 文件总体积：15.52 MB
• 源站页面：https://frontiersin.figshare.com/articles/dataset/Data_Sheet_1_External_validation_and_application_of_a_machine_learning_based_model_for_diabetes_progression_in_prediabetes_docx/31833307

命令行下载

curl -L -o "Data Sheet 1_External validation and application of a machine learning–based model for diabetes progression in prediabetes.docx" "https://ndownloader.figshare.com/files/63013525"

文件清单

• Data Sheet 1_External validation and application of a machine learning–based model for diabetes progression in prediabetes.docx（15.52 MB）

文件总体积 15.52 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

IntroductionThis study externally validated a machine learning–based model for type 2 diabetes progression (ML-PR) and evaluated its clinical utility in individuals with prediabetes.
MethodsWe included 3,081 participants from the Diabetes Prevention Program (DPP) and the DPP Outcome Study (DPPOS). The ML-PR model was assessed using dicrimination, calibration curves, and decision curve analysis, and its performance was compared with existing diabetes prediction models. Based on ML-PR scores, patients were stratified into high- or low-risk categories. Cox proportional hazards and logistic regression models were used to evaluate the incidence of type 2 diabetes, microvascular complications, and cardiovascular events across risk and intervention groups.
ResultsThe ML-PR model achieved an area under the ROC curve of 0.74 (95% confidence interval: 0.71–0.78) for predicting 3-year progression to type 2 diabetes. Calibration and decision curve analyses indicated good agreement and net clinical benefit. High-risk individuals exhibited a significantly higher risk of developing type 2 diabetes in both the DPP and DPPOS cohorts (P Externally validated in U.S. cohorts, the ML-PR model effectively identifies individuals with prediabetes at elevated risk of diabetes progressing and microvascular complications. These findings suggest that intensive lifestyle interventions and metformin therapy may be particularly beneficial for individuals at higher risk, highlighting the potential for more precise treatment strategies in type 2 diabetes.

许可

CC BY 4.0

引用

学术使用请引用 DOI 10.3389/fendo.2026.1746570.s001（source: https://frontiersin.figshare.com/articles/dataset/Data_Sheet_1_External_validation_and_application_of_a_machine_learning_based_model_for_diabetes_progression_in_prediabetes_docx/31833307）。