2型糖尿病患者全因死亡风险预测模型数据表

数据集获取（Figshare）

• 源站标题：Table 3_Explainable artificial intelligence model predicting the risk of all-cause mortality in patients with type 2 diabetes mellitus.xlsx
• DOI：10.3389/fendo.2025.1689312.s003
• 发布日期：2025-10-17
• 文件数：1 个
• 文件总体积：12.25 KB
• 源站页面：https://frontiersin.figshare.com/articles/dataset/Table_3_Explainable_artificial_intelligence_model_predicting_the_risk_of_all-cause_mortality_in_patients_with_type_2_diabetes_mellitus_xlsx/30381688

命令行下载

curl -L -o "Table 3_Explainable artificial intelligence model predicting the risk of all-cause mortality in patients with type 2 diabetes mellitus.xlsx" "https://ndownloader.figshare.com/files/58819330"

文件清单

• Table 3_Explainable artificial intelligence model predicting the risk of all-cause mortality in patients with type 2 diabetes mellitus.xlsx（12.25 KB）

文件总体积 12.25 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

BackgroundType 2 diabetes mellitus (T2DM) is a highly prevalent non-communicable chronic disease that substantially reduces life expectancy. Accurate estimation of all-cause mortality risk in T2DM patients is crucial for personalizing and optimizing treatment strategies.
MethodsThis study analyzed a cohort of 554 patients (aged 40–87 years) with diagnosed T2DM over a maximum follow-up period of 16.8 years, during which 202 patients (36%) died. Key survival-associated features were identified, and multiple machine learning (ML) models were trained and validated to predict all-cause mortality risk. To improve model interpretability, Shapley additive explanations (SHAP) was applied to the best-performing model.
ResultsThe extra survival trees (EST) model, incorporating ten key features, demonstrated the best predictive performance. The model achieved a C-statistic of 0.776, with the area under the receiver operating characteristic curve (AUC) values of 0.86, 0.80, 0.841, and 0.826 for 5-, 10-, 15-, and 16.8-year all-cause mortality predictions, respectively. The SHAP approach was employed to interpret the model’s individual decision-making processes.
ConclusionThe developed model exhibited strong predictive performance for mortality risk assessment. Its clinically interpretable outputs enable potential bedside application, improving the identification of high-risk patients and supporting timely treatment optimization.

许可

CC BY 4.0

引用

学术使用请引用 DOI 10.3389/fendo.2025.1689312.s003（source: https://frontiersin.figshare.com/articles/dataset/Table_3_Explainable_artificial_intelligence_model_predicting_the_risk_of_all-cause_mortality_in_patients_with_type_2_diabetes_mellitus_xlsx/30381688）。