气象因素对动脉瘤性蛛网膜下腔出血发病率的影响——一项511例患者的单中心研究

数据集获取（Figshare）

• 源站标题：Meteorological Influences on the Incidence of Aneurysmal Subarachnoid Hemorrhage – A Single Center Study of 511 Patients
• DOI：10.1371/journal.pone.0081621
• 发布日期：2013-12-02
• 文件数：5 个
• 文件总体积：1.10 MB
• 源站页面：https://plos.figshare.com/articles/dataset/_Meteorological_Influences_on_the_Incidence_of_Aneurysmal_Subarachnoid_Hemorrhage_8211_A_Single_Center_Study_of_511_Patients_/866242

命令行下载

curl -L -o "Figure_S1.pdf" "https://ndownloader.figshare.com/files/1300388"
curl -L -o "Figure_S2.pdf" "https://ndownloader.figshare.com/files/1300389"
curl -L -o "Figure_S3.pdf" "https://ndownloader.figshare.com/files/1300391"
curl -L -o "Figure_S4.pdf" "https://ndownloader.figshare.com/files/1300392"
curl -L -o "Figure_S5.pdf" "https://ndownloader.figshare.com/files/1300393"

文件清单

• Figure_S1.pdf（324.97 KB）
• Figure_S2.pdf（172.38 KB）
• Figure_S3.pdf（173.77 KB）
• Figure_S4.pdf（215.50 KB）
• Figure_S5.pdf（215.27 KB）

文件总体积 1.10 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

Objective
To assess the potential meteorological influence on the incidence of aneurysmal subarachnoid hemorrhage (SAH). Previous studies used inhomogeneous patient groups, insufficient study periods or inappropriate statistics.
Patients and Methods
We analyzed 511 SAH admissions between 2004 and 2012 for which aneurysmal rupture occurred within the Zurich region. The hourly meteorological parameters considered are: surface pressure, 2-m temperature, relative humidity and wind gusts, sunshine, and precipitation. For all parameters we investigate three complementary statistical measures: i) the time evolution from 5 days before to 5 days after the SAH occurrence; ii) the deviation from the 10-year monthly mean; and iii) the change relative to the parameter’'s value two days before SAH occurrence. The statistical significance of the results is determined using a Monte Carlo simulation combined with a re-sampling technique (1000×).
Results
Regarding the meteorological parameters considered, no statistically significant signal could be found. The distributions of deviations relative to the climatology and of the changes during the two days prior to SAH events agree with the distributions for the randomly chosen days. The analysis was repeated separately for winter and summer to exclude compensating effects between the seasons.
Conclusion
By using high-quality meteorological data analyzed with a sophisticated and robust statistical method no clearly identifiable meteorological influence for the SAH events considered can be found. Further studies on the influence of the investigated parameters on SAH incidence seem redundant.

许可

CC BY 4.0

引用

学术使用请引用 DOI 10.1371/journal.pone.0081621（source: https://plos.figshare.com/articles/dataset/_Meteorological_Influences_on_the_Incidence_of_Aneurysmal_Subarachnoid_Hemorrhage_8211_A_Single_Center_Study_of_511_Patients_/866242）。