英国已故供者肾移植结局预测工具（UK-DTOP）

数据集获取（Figshare）

• 源站标题：Improved survival prediction for kidney transplant outcomes using artificial intelligence-based models: development of the UK Deceased Donor Kidney Transplant Outcome Prediction (UK-DTOP) Tool
• DOI：10.6084/m9.figshare.27281036.v1
• 发布日期：2024-10-22
• 文件数：2 个
• 文件总体积：37.20 KB
• 源站页面：https://tandf.figshare.com/articles/dataset/Improved_survival_prediction_for_kidney_transplant_outcomes_using_artificial_intelligence-based_models_development_of_the_UK_Deceased_Donor_Kidney_Transplant_Outcome_Prediction_UK-DTOP_Tool/27281036

命令行下载

curl -L -o "Supplementary file 1.docx" "https://ndownloader.figshare.com/files/49931878"
curl -L -o "supplementary file 2.docx" "https://ndownloader.figshare.com/files/49931881"

文件清单

• Supplementary file 1.docx（18.89 KB）
• supplementary file 2.docx（18.30 KB）

文件总体积 37.20 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

The UK Deceased Donor Kidney Transplant Outcome Prediction (UK-DTOP) Tool, developed using advanced artificial intelligence (AI), significantly enhances the prediction of outcomes for deceased-donor kidney transplants in the UK. This study analyzed data from the UK Transplant Registry (UKTR), including 29,713 transplant cases between 2008 and 2022, to assess the predictive performance of three machine learning models: XGBoost, Random Survival Forest, and Optimal Decision Tree. Among these, XGBoost demonstrated exceptional performance with the highest concordance index of 0.74 and an area under the curve (AUC) consistently above 0.73, indicating robust discriminative ability and calibration. In comparison to the traditional Kidney Donor Risk Index (KDRI), which achieved a lower concordance index of 0.57, the UK-DTOP model marked a significant improvement, underscoring its superior predictive accuracy. The advanced capabilities of the XGBoost model were further highlighted through calibration assessments using the Integrated Brier Score (IBS), showing a score of 0.14, indicative of precise survival probability predictions. Additionally, unsupervised learning via k-means clustering was employed to identify five distinct clusters based on donor and transplant characteristics, uncovering nuanced insights into graft survival outcomes. These clusters were further analyzed using Bayesian Cox regression, which confirmed significant survival outcome variations across the clusters, thereby validating the model’s effectiveness in enhancing risk stratification. The UK-DTOP tool offers a comprehensive decision-support system that significantly refines pre-transplant decision-making. The study’s findings advocate for the adoption of AI-enhanced tools in healthcare systems to optimize organ matching and transplant success, potentially guiding future developments in global transplant practices.

许可

CC BY 4.0

引用

学术使用请引用 DOI 10.6084/m9.figshare.27281036.v1（source: https://tandf.figshare.com/articles/dataset/Improved_survival_prediction_for_kidney_transplant_outcomes_using_artificial_intelligence-based_models_development_of_the_UK_Deceased_Donor_Kidney_Transplant_Outcome_Prediction_UK-DTOP_Tool/27281036）。