主动脉夹层风险预测的机器学习方法系统综述与荟萃分析表1

数据集获取（Figshare）

• 源站标题：Table 1_Machine learning approaches for risk prediction in aortic dissection: a systematic review and meta-analysis.docx
• DOI：10.3389/fcvm.2026.1777734.s015
• 发布日期：2026-03-26
• 文件数：1 个
• 文件总体积：23.87 KB
• 源站页面：https://frontiersin.figshare.com/articles/dataset/Table_1_Machine_learning_approaches_for_risk_prediction_in_aortic_dissection_a_systematic_review_and_meta-analysis_docx/31858483

命令行下载

curl -L -o "Table 1_Machine learning approaches for risk prediction in aortic dissection: a systematic review and meta-analysis.docx" "https://ndownloader.figshare.com/files/63114163"

文件清单

• Table 1_Machine learning approaches for risk prediction in aortic dissection: a systematic review and meta-analysis.docx（23.87 KB）

文件总体积 23.87 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

BackgroundAortic dissection (AD) is a life-threatening cardiovascular emergency with high morbidity and mortality. Accurate risk prediction is essential for timely intervention, yet traditional statistical models often fail to capture the complex, nonlinear interactions inherent in AD pathophysiology. In recent years, machine learning (ML) has emerged as a promising approach to improve prognostic accuracy. However, the overall performance, methodological quality, and clinical applicability of ML-based prediction models for AD have not been comprehensively evaluated.
ObjectiveThis systematic review and meta-analysis followed PRISMA, CHARMS, and TRIPOD guidelines and was registered with PROSPERO (CRD420251154262). Six major databases (PubMed, Web of Science, Cochrane Library, Embase, CNKI, Wanfang) were searched from inception to September 30, 2025. Studies developing or validating ML models for predicting adverse outcomes in AD were included. Data extraction adhered to CHARMS, and risk of bias was assessed using PROBAST. Meta-analysis synthesized C-statistics (AUC) using fixed- or random-effects models depending on heterogeneity. Subgroup, sensitivity, and publication bias analyses were performed.
ResultsForty studies were included, covering outcomes such as early mortality, long-term mortality, acute kidney injury (AKI), neurological complications, gastrointestinal bleeding, mesenteric malperfusion, and composite adverse events. ML algorithms included random forest, SVM, XGBoost, LightGBM, neural networks, and ensemble approaches. The pooled C-statistic demonstrated excellent discriminative performance for early mortality (0.891, 95% CI: 0.854–0.927) and long-term mortality (0.847, 95% CI: 0.794–0.900), and strong performance for AKI prediction (0.825, 95% CI: 0.756–0.894). Many complication-specific models achieved AUCs above 0.90. However, these estimates must be interpreted with extreme caution. Significant heterogeneity was observed across analyses (I2 = 61.3–78.8%), and the PROBAST assessment revealed that 100% (40/40) of studies were at high or unclear risk of bias, predominantly due to deficiencies in the analysis domain (e.g., inadequate events-per-varia

许可

CC BY 4.0

引用

学术使用请引用 DOI 10.3389/fcvm.2026.1777734.s015（source: https://frontiersin.figshare.com/articles/dataset/Table_1_Machine_learning_approaches_for_risk_prediction_in_aortic_dissection_a_systematic_review_and_meta-analysis_docx/31858483）。