透析患者心血管事件预测的机器学习与传统统计模型系统回顾与比较

数据集获取（Figshare）

• 源站标题：Systematic review and comparison of machine learning and conventional statistical models for predicting cardiovascular events in dialysis patients
• DOI：10.6084/m9.figshare.30662224.v1
• 发布日期：2025-11-20
• 文件数：5 个
• 文件总体积：1.42 MB
• 源站页面：https://tandf.figshare.com/articles/dataset/Systematic_review_and_comparison_of_machine_learning_and_conventional_statistical_models_for_predicting_cardiovascular_events_in_dialysis_patients/30662224

命令行下载

curl -L -o "irnf_a_2587490_sm5674.pdf" "https://ndownloader.figshare.com/files/59710258"
curl -L -o "irnf_a_2587490_sm5673.pdf" "https://ndownloader.figshare.com/files/59710261"
curl -L -o "irnf_a_2587490_sm5672.pdf" "https://ndownloader.figshare.com/files/59710264"
curl -L -o "irnf_a_2587490_sm5671.pdf" "https://ndownloader.figshare.com/files/59710267"
curl -L -o "irnf_a_2587490_sm5670.pdf" "https://ndownloader.figshare.com/files/59710270"

文件清单

• irnf_a_2587490_sm5674.pdf（900.31 KB）
• irnf_a_2587490_sm5673.pdf（45.26 KB）
• irnf_a_2587490_sm5672.pdf（277.30 KB）
• irnf_a_2587490_sm5671.pdf（73.02 KB）
• irnf_a_2587490_sm5670.pdf（124.81 KB）

文件总体积 1.42 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

This systematic review aimed to evaluate the performance of machine learning (ML) models and conventional statistical models (CSMs) for predicting cardiovascular events in dialysis patients. Following PRISMA guidelines, eligible studies were searched through PubMed and Embase (January 2015–March 2025). Model performance (AUC/C-index) was compared using Mann–Whitney U test, while risk of bias was assessed via PROBAST. Furthermore, subgroup analyses stratified by algorithm type, validation method, and dataset size were conducted to explore heterogeneity. The review included 14 studies encompassing 29,310 patients and 34 models. Based on test/validation datasets only, ML models achieved comparable discrimination (mean AUC: 0.784 ± 0.112) than CSMs (0.772 ± 0.066), without statistical significance (p = 0.24). The PROBAST assessment indicated that 71.43% of studies had a low risk of bias. Subgroup analysis of performance revealed that deep learning models significantly outperformed both traditional ML and CSMs (p = 0.005), whereas traditional ML showed no advantage over CSMs (p = 0.727). Studies were predominantly originated from China (71.40%) and relied on internal validation (78.57%), limiting generalizability. Although deep learning algorithms show promises, ML models overall do not significantly outperform CSMs. CSMs remain viable, especially in resource-limited settings. Critical limitations include geographical bias, insufficient external validation, and tradeoffs between accuracy and interpretability. Future research should prioritize validation frameworks and clinical implementation over marginal accuracy improvements.

许可

CC BY 4.0

引用

学术使用请引用 DOI 10.6084/m9.figshare.30662224.v1（source: https://tandf.figshare.com/articles/dataset/Systematic_review_and_comparison_of_machine_learning_and_conventional_statistical_models_for_predicting_cardiovascular_events_in_dialysis_patients/30662224）。