利用机器学习分析天气条件预测急性冠脉综合征患病率的数据表

数据集获取（Figshare）

• 源站标题：Data_Sheet_1_Machine Learning Analyzed Weather Conditions as an Effective Means in the Predicting of Acute Coronary Syndrome Prevalence.docx
• DOI：10.3389/fcvm.2022.830823.s001
• 发布日期：2022-04-08
• 文件数：1 个
• 文件总体积：1.18 MB
• 源站页面：https://frontiersin.figshare.com/articles/dataset/Data_Sheet_1_Machine_Learning_Analyzed_Weather_Conditions_as_an_Effective_Means_in_the_Predicting_of_Acute_Coronary_Syndrome_Prevalence_docx/19546495

命令行下载

curl -L -o "Data_Sheet_1_Machine Learning Analyzed Weather Conditions as an Effective Means in the Predicting of Acute Coronary Syndrome Prevalence.docx" "https://ndownloader.figshare.com/files/34733215"

文件清单

• Data_Sheet_1_Machine Learning Analyzed Weather Conditions as an Effective Means in the Predicting of Acute Coronary Syndrome Prevalence.docx（1.18 MB）

文件总体积 1.18 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

BackgroundThe prediction of the number of acute coronary syndromes (ACSs) based on the weather conditions in the individual climate zones is not effective. We sought to investigate whether an artificial intelligence system might be useful in this prediction.
MethodsBetween 2008 and 2018, a total of 105,934 patients with ACS were hospitalized in Lesser Poland Province, one covered by two meteorological stations. The predicted daily number of ACS has been estimated with the Random Forest machine learning system based on air temperature (°C), air pressure (hPa), dew point temperature (Td) (°C), relative humidity (RH) (%), wind speed (m/s), and precipitation (mm) and their daily extremes and ranges derived from the day of ACS and from 6 days before ACS.
ResultsOf 840 pairwise comparisons between individual weather parameters and the number of ACS, 128 (15.2%) were significant but weak with the correlation coefficients ranged from −0.16 to 0.16. None of weather parameters correlated with the number of ACS in all the seasons and stations. The number of ACS was higher in warm front days vs. days without any front [40 (29–50) vs. 38 (27–48), respectively, P The weather parameters have proven useful in predicting the prevalence of ACS in a temperate climate zone for all the seasons, if analyzed with an artificial intelligence system. Simultaneously, the analysis of individual weather parameters or frontal scenarios has provided only weak univariate relationships. These findings will require validation in other climatic zones.

许可

CC BY 4.0

引用

学术使用请引用 DOI 10.3389/fcvm.2022.830823.s001（source: https://frontiersin.figshare.com/articles/dataset/Data_Sheet_1_Machine_Learning_Analyzed_Weather_Conditions_as_an_Effective_Means_in_the_Predicting_of_Acute_Coronary_Syndrome_Prevalence_docx/19546495）。