预测透析中低血压的深度学习模型数据表1

数据集获取（Figshare）

• 源站标题：Data_Sheet_1_Deep Learning Model for Predicting Intradialytic Hypotension Without Privacy Infringement: A Retrospective Two-Center Study.docx
• DOI：10.3389/fmed.2022.878858.s001
• 发布日期：2022-07-07
• 文件数：1 个
• 文件总体积：1.89 MB
• 源站页面：https://frontiersin.figshare.com/articles/dataset/Data_Sheet_1_Deep_Learning_Model_for_Predicting_Intradialytic_Hypotension_Without_Privacy_Infringement_A_Retrospective_Two-Center_Study_docx/20250792

命令行下载

curl -L -o "Data_Sheet_1_Deep Learning Model for Predicting Intradialytic Hypotension Without Privacy Infringement: A Retrospective Two-Center Study.docx" "https://ndownloader.figshare.com/files/36185400"

文件清单

• Data_Sheet_1_Deep Learning Model for Predicting Intradialytic Hypotension Without Privacy Infringement: A Retrospective Two-Center Study.docx（1.89 MB）

文件总体积 1.89 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

ObjectivePreviously developed Intradialytic hypotension (IDH) prediction models utilize clinical variables with potential privacy protection issues. We developed an IDH prediction model using minimal variables, without the risk of privacy infringement.
MethodsUnidentifiable data from 63,640 hemodialysis sessions (26,746 of 79 patients for internal validation, 36,894 of 255 patients for external validation) from two Korean hospital hemodialysis databases were finally analyzed, using three IDH definitions: (1) systolic blood pressure (SBP) nadir Among 344,714 segments, 9,154 (2.7%), 134,988 (39.2%), and 149,674 (43.4%) IDH events occurred according to three different IDH definitions (Nadir90, Fall20, and Fall20/MAP10, respectively). Compared with models including logistic regression, random forest, and XGBoost, the deep learning model achieved the best performance in predicting IDH (AUROCs: Nadir90, 0.905; Fall20, 0.864; Fall20/MAP10, 0.863) only using measurements from hemodialysis machine during dialysis session.
ConclusionsThe deep learning model performed well only using monitoring measurement of hemodialysis machine in predicting IDH without any personal information that could risk privacy infringement.

许可

CC BY 4.0

引用

学术使用请引用 DOI 10.3389/fmed.2022.878858.s001（source: https://frontiersin.figshare.com/articles/dataset/Data_Sheet_1_Deep_Learning_Model_for_Predicting_Intradialytic_Hypotension_Without_Privacy_Infringement_A_Retrospective_Two-Center_Study_docx/20250792）。