在初级保健电子病历数据库中识别脊髓损伤或疾病病例

数据集获取（Figshare）

• 源站标题：Identifying cases of spinal cord injury or disease in a primary care electronic medical record database
• DOI：10.6084/m9.figshare.17013913.v1
• 发布日期：2021-11-15
• 文件数：2 个
• 文件总体积：68.07 KB
• 源站页面：https://tandf.figshare.com/articles/dataset/Identifying_cases_of_spinal_cord_injury_or_disease_in_a_primary_care_electronic_medical_record_database/17013913

命令行下载

curl -L -o "JSCM_Supplemental Table 1-JShepherd.docx" "https://ndownloader.figshare.com/files/31469371"
curl -L -o "JSCM_Supplemental Table 2-JShepherd.docx" "https://ndownloader.figshare.com/files/31469374"

文件清单

• JSCM_Supplemental Table 1-JShepherd.docx（22.52 KB）
• JSCM_Supplemental Table 2-JShepherd.docx（45.55 KB）

文件总体积 68.07 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

To identify cases of spinal cord injury or disease (SCI/D) in an Ontario database of primary care electronic medical records (EMR).
A reference standard of cases of chronic SCI/D was established via manual review of EMRs; this reference standard was used to evaluate potential case identification algorithms for use in the same database.
Electronic Medical Records Primary Care (EMRPC) Database, Ontario, Canada.
A sample of 48,000 adult patients was randomly selected from 213,887 eligible patients in the EMRPC database.
N/A.
Candidate algorithms were evaluated using sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), and F-score.
126 cases of chronic SCI/D were identified, forming the reference standard. Of these, 57 were cases of traumatic spinal cord injury (TSCI), and 67 were cases of non-traumatic spinal cord injury (NTSCI). The optimal case identification algorithm used free-text keyword searches and a physician billing code, and had 70.6% sensitivity (61.9–78.4), 98.5% specificity (97.3–99.3), 89.9% PPV (82.2–95.0), 94.7% NPV (92.8–96.3), and an F-score of 79.1.
Identifying cases of chronic SCI/D from a database of primary care EMRs using free-text entries is feasible, relying on a comprehensive case definition. Identifying a cohort of patients with SCI/D will allow for future study of the epidemiology and health service utilization of these patients.

许可

CC BY 4.0

引用

学术使用请引用 DOI 10.6084/m9.figshare.17013913.v1（source: https://tandf.figshare.com/articles/dataset/Identifying_cases_of_spinal_cord_injury_or_disease_in_a_primary_care_electronic_medical_record_database/17013913）。