Table 1：肺栓塞可能性深度学习增强预测模型的开发与验证

数据集获取（Figshare）

• 源站标题：Table 1_Development and validation of a deep learning-enhanced prediction model for the likelihood of pulmonary embolism.docx
• DOI：10.3389/fmed.2025.1506363.s001
• 发布日期：2025-02-06
• 文件数：1 个
• 文件总体积：38.31 KB
• 源站页面：https://frontiersin.figshare.com/articles/dataset/Table_1_Development_and_validation_of_a_deep_learning-enhanced_prediction_model_for_the_likelihood_of_pulmonary_embolism_docx/28357748

命令行下载

curl -L -o "Table 1_Development and validation of a deep learning-enhanced prediction model for the likelihood of pulmonary embolism.docx" "https://ndownloader.figshare.com/files/52166207"

文件清单

• Table 1_Development and validation of a deep learning-enhanced prediction model for the likelihood of pulmonary embolism.docx（38.31 KB）

文件总体积 38.31 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

BackgroundPulmonary embolism (PE) is a common and potentially fatal condition. Timely and accurate risk assessment in patients with acute deep vein thrombosis (DVT) is crucial. This study aims to develop a deep learning-based, precise, and efficient PE risk prediction model (PE-Mind) to overcome the limitations of current clinical tools and provide a more targeted risk evaluation solution.
MethodsWe analyzed clinical data from patients by first simplifying and organizing the collected features. From these, 37 key clinical features were selected based on their importance. These features were categorized and analyzed to identify potential relationships. Our prediction model uses a convolutional neural network (CNN), enhanced with three custom-designed modules for better performance. To validate its effectiveness, we compared this model with five commonly used prediction models.
ResultsPE-Mind demonstrated the highest accuracy and reliability, achieving 0.7826 accuracy and an area under the receiver operating characteristic curve of 0.8641 on the prospective test set, surpassing other models. Based on this, we have also developed a Web server, PulmoRiskAI, for real-time clinician operation.
ConclusionThe PE-Mind model improves prediction accuracy and reliability for assessing PE risk in acute DVT patients. Its convolutional architecture and residual modules substantially enhance predictive performance.

许可

CC BY 4.0

引用

学术使用请引用 DOI 10.3389/fmed.2025.1506363.s001（source: https://frontiersin.figshare.com/articles/dataset/Table_1_Development_and_validation_of_a_deep_learning-enhanced_prediction_model_for_the_likelihood_of_pulmonary_embolism_docx/28357748）。