高容量电子医疗理赔数据监控美国本地和区域流感活动的演示

数据集获取（Figshare）

• 源站标题：Demonstrating the Use of High-Volume Electronic Medical Claims Data to Monitor Local and Regional Influenza Activity in the US
• DOI：10.1371/journal.pone.0102429
• 发布日期：2014-07-29
• 文件数：5 个
• 文件总体积：9.51 MB
• 源站页面：https://plos.figshare.com/articles/dataset/_Demonstrating_the_Use_of_High_Volume_Electronic_Medical_Claims_Data_to_Monitor_Local_and_Regional_Influenza_Activity_in_the_US_/1120733

命令行下载

curl -L -o "Figure_S1.tif" "https://ndownloader.figshare.com/files/1613001"
curl -L -o "Figure_S2.tif" "https://ndownloader.figshare.com/files/1613002"
curl -L -o "Figure_S3.tif" "https://ndownloader.figshare.com/files/1613003"
curl -L -o "Figure_S4.tif" "https://ndownloader.figshare.com/files/1613004"
curl -L -o "Text_S1.doc" "https://ndownloader.figshare.com/files/1613005"

文件清单

• Figure_S1.tif（2.28 MB）
• Figure_S2.tif（1.19 MB）
• Figure_S3.tif（2.95 MB）
• Figure_S4.tif（2.98 MB）
• Text_S1.doc（102.40 KB）

文件总体积 9.51 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

Introduction
Fine-grained influenza surveillance data are lacking in the US, hampering our ability to monitor disease spread at a local scale. Here we evaluate the performances of high-volume electronic medical claims data to assess local and regional influenza activity.
Material and Methods
We used electronic medical claims data compiled by IMS Health in 480 US locations to create weekly regional influenza-like-illness (ILI) time series during 2003–2010. IMS Health captured 62% of US outpatient visits in 2009. We studied the performances of IMS-ILI indicators against reference influenza surveillance datasets, including CDC-ILI outpatient and laboratory-confirmed influenza data. We estimated correlation in weekly incidences, peak timing and seasonal intensity across datasets, stratified by 10 regions and four age groups (Results
Regional IMS-ILI indicators were highly synchronous with CDC’'s reference influenza surveillance data (Pearson correlation coefficients rho≥0.89; range across regions, 0.80–0.97, PConclusion
Medical claims-based ILI indicators accurately capture weekly fluctuations in influenza activity in all US regions during inter-pandemic and pandemic seasons, and can be broken down by age groups and fine geographical areas. Medical claims data provide more reliable and fine-grained indicators of influenza activity than other high-volume electronic algorithms and should be used to augment existing influenza surveillance systems.

许可

CC BY 4.0

引用

学术使用请引用 DOI 10.1371/journal.pone.0102429（source: https://plos.figshare.com/articles/dataset/_Demonstrating_the_Use_of_High_Volume_Electronic_Medical_Claims_Data_to_Monitor_Local_and_Regional_Influenza_Activity_in_the_US_/1120733）。