从电子病历中提取原发性开角型青光眼用于遗传关联研究

数据集获取（Figshare）

• 源站标题：Extracting Primary Open-Angle Glaucoma from Electronic Medical Records for Genetic Association Studies
• DOI：10.1371/journal.pone.0127817
• 发布日期：2015-06-10
• 文件数：6 个
• 文件总体积：513.08 KB
• 源站页面：https://plos.figshare.com/articles/dataset/_Extracting_Primary_Open_Angle_Glaucoma_from_Electronic_Medical_Records_for_Genetic_Association_Studies_/1445889

命令行下载

curl -L -o "S1_Table.docx" "https://ndownloader.figshare.com/files/2106618"
curl -L -o "S2_Table.docx" "https://ndownloader.figshare.com/files/2106619"
curl -L -o "S3_Table.docx" "https://ndownloader.figshare.com/files/2106620"
curl -L -o "S1_Fig.tif" "https://ndownloader.figshare.com/files/2106621"
curl -L -o "S2_Fig.tif" "https://ndownloader.figshare.com/files/2106622"
curl -L -o "S3_Fig.tif" "https://ndownloader.figshare.com/files/2106623"

文件清单

• S1_Table.docx（15.20 KB）
• S2_Table.docx（15.53 KB）
• S3_Table.docx（16.27 KB）
• S1_Fig.tif（152.40 KB）
• S2_Fig.tif（159.78 KB）
• S3_Fig.tif（153.90 KB）

文件总体积 513.08 KB（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

Electronic medical records (EMRs) are being widely implemented for use in genetic and genomic studies. As a phenotypic rich resource, EMRs provide researchers with the opportunity to identify disease cohorts and perform genotype-phenotype association studies. The Epidemiologic Architecture for Genes Linked to Environment (EAGLE) study, as part of the Population Architecture using Genomics and Epidemiology (PAGE) I study, has genotyped more than 15,000 individuals of diverse genetic ancestry in BioVU, the Vanderbilt University Medical Center’s biorepository linked to a de-identified version of the EMR (EAGLE BioVU). Here we develop and deploy an algorithm utilizing data mining techniques to identify primary open-angle glaucoma (POAG) in African Americans from EAGLE BioVU for genetic association studies. The algorithm described here was designed using a combination of diagnostic codes, current procedural terminology billing codes, and free text searches to identify POAG status in situations where gold-standard digital photography cannot be accessed. The case algorithm identified 267 potential POAG subjects but underperformed after manual review with a positive predictive value of 51.6% and an accuracy of 76.3%. The control algorithm identified controls with a negative predictive value of 98.3%. Although the case algorithm requires more downstream manual review for use in large-scale studies, it provides a basis by which to extract a specific clinical subtype of glaucoma from EMRs in the absence of digital photographs.

许可

CC BY 4.0

引用

学术使用请引用 DOI 10.1371/journal.pone.0127817（source: https://plos.figshare.com/articles/dataset/_Extracting_Primary_Open_Angle_Glaucoma_from_Electronic_Medical_Records_for_Genetic_Association_Studies_/1445889）。