医院获得性感染的传播：医疗网络的比较

数据集获取（Figshare）

• 源站标题：Spread of hospital-acquired infections: A comparison of healthcare networks
• DOI：10.1371/journal.pcbi.1005666
• 发布日期：2017-08-24
• 文件数：21 个
• 文件总体积：3.04 MB
• 源站页面：https://plos.figshare.com/articles/dataset/Spread_of_hospital-acquired_infections_A_comparison_of_healthcare_networks/5341441

命令行下载

curl -L -o "S1 Annex.pdf" "https://ndownloader.figshare.com/files/9181906"
curl -L -o "S1 Text.pdf" "https://ndownloader.figshare.com/files/9181915"
curl -L -o "S2 Text.pdf" "https://ndownloader.figshare.com/files/9181921"
curl -L -o "S3 Text.pdf" "https://ndownloader.figshare.com/files/9181927"
curl -L -o "S4 Text.pdf" "https://ndownloader.figshare.com/files/9181933"
curl -L -o "S5 Text.pdf" "https://ndownloader.figshare.com/files/9181936"
curl -L -o "S6 Text.pdf" "https://ndownloader.figshare.com/files/9181945"
curl -L -o "S7 Text.pdf" "https://ndownloader.figshare.com/files/9181957"

文件清单

• S1 Annex.pdf（105.78 KB）
• S1 Text.pdf（115.60 KB）
• S2 Text.pdf（59.97 KB）
• S3 Text.pdf（58.67 KB）
• S4 Text.pdf（59.61 KB）
• S5 Text.pdf（51.08 KB）
• S6 Text.pdf（58.88 KB）
• S7 Text.pdf（54.41 KB）
• S1 Fig.pdf（141.49 KB）
• S2 Fig.pdf（147.16 KB）
• S3 Fig.pdf（141.25 KB）
• S4 Fig.pdf（202.79 KB）
• S5 Fig.pdf（95.51 KB）
• S6 Fig.pdf（97.08 KB）
• S7 Fig.pdf（96.69 KB）
• S8 Fig.pdf（47.10 KB）
• S9 Fig.pdf（47.99 KB）
• S10 Fig.pdf（907.60 KB）
• S11 Fig.pdf（268.31 KB）
• S12 Fig.pdf（205.96 KB）

文件总体积 3.04 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

Hospital-acquired infections (HAIs), including emerging multi-drug resistant organisms, threaten healthcare systems worldwide. Efficient containment measures of HAIs must mobilize the entire healthcare network. Thus, to best understand how to reduce the potential scale of HAI epidemic spread, we explore patient transfer patterns in the French healthcare system. Using an exhaustive database of all hospital discharge summaries in France in 2014, we construct and analyze three patient networks based on the following: transfers of patients with HAI (HAI-specific network); patients with suspected HAI (suspected-HAI network); and all patients (general network). All three networks have heterogeneous patient flow and demonstrate small-world and scale-free characteristics. Patient populations that comprise these networks are also heterogeneous in their movement patterns. Ranking of hospitals by centrality measures and comparing community clustering using community detection algorithms shows that despite the differences in patient population, the HAI-specific and suspected-HAI networks rely on the same underlying structure as that of the general network. As a result, the general network may be more reliable in studying potential spread of HAIs. Finally, we identify transfer patterns at both the French regional and departmental (county) levels that are important in the identification of key hospital centers, patient flow trajectories, and regional clusters that may serve as a basis for novel wide-scale infection control strategies.

许可

CC BY 4.0

引用

学术使用请引用 DOI 10.1371/journal.pcbi.1005666（source: https://plos.figshare.com/articles/dataset/Spread_of_hospital-acquired_infections_A_comparison_of_healthcare_networks/5341441）。