自我报告合并症与药物评估的系统分析

数据集获取（Figshare）

• 源站标题：Systematic Analysis of Self-Reported Comorbidities in Large Cohort Studies – A Novel Stepwise Approach by Evaluation of Medication
• DOI：10.1371/journal.pone.0163408
• 发布日期：2016-10-28
• 文件数：15 个
• 文件总体积：331.74 KB
• 源站页面：https://plos.figshare.com/articles/dataset/Systematic_Analysis_of_Self-Reported_Comorbidities_in_Large_Cohort_Studies_A_Novel_Stepwise_Approach_by_Evaluation_of_Medication/4128705

命令行下载

curl -L -o "S1 COSYCONET Consortium.DOCX" "https://ndownloader.figshare.com/files/6699975"
curl -L -o "S1 Ethics Committees.DOCX" "https://ndownloader.figshare.com/files/6699981"
curl -L -o "S1 Fig.TIF" "https://ndownloader.figshare.com/files/6699984"
curl -L -o "S1 Table.DOCX" "https://ndownloader.figshare.com/files/6699990"
curl -L -o "S2 Table.DOCX" "https://ndownloader.figshare.com/files/6699993"
curl -L -o "S3 Table.DOCX" "https://ndownloader.figshare.com/files/6699999"
curl -L -o "S4 Table.DOCX" "https://ndownloader.figshare.com/files/6700002"
curl -L -o "S5 Table.DOCX" "https://ndownloader.figshare.com/files/6700008"

文件清单

• S1 COSYCONET Consortium.DOCX（22.33 KB）
• S1 Ethics Committees.DOCX（21.78 KB）
• S1 Fig.TIF（91.46 KB）
• S1 Table.DOCX（16.21 KB）
• S2 Table.DOCX（16.94 KB）
• S3 Table.DOCX（19.57 KB）
• S4 Table.DOCX（14.58 KB）
• S5 Table.DOCX（15.54 KB）
• S6 Table.DOCX（17.20 KB）
• S7 Table.DOCX（14.23 KB）
• S8 Table.DOCX（14.17 KB）
• S9 Table.DOCX（14.37 KB）
• S10 Table.DOCX（15.89 KB）
• S11 Table.DOCX（16.02 KB）
• S12 Table.DOCX（21.45 KB）

文件总体积 331.74 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

Objective
In large cohort studies comorbidities are usually self-reported by the patients. This way to collect health information only represents conditions known, memorized and openly reported by the patients. Several studies addressed the relationship between self-reported comorbidities and medical records or pharmacy data, but none of them provided a structured, documented method of evaluation. We thus developed a detailed procedure to compare self-reported comorbidities with information on comorbidities derived from medication inspection. This was applied to the data of the German COPD cohort COSYCONET.
Methods
Approach I was based solely on ICD10-Codes for the diseases and the indications of medications. To overcome the limitations due to potential non-specificity of medications, Approach II was developed using more detailed information, such as ATC-Codes specific for one disease. The relationship between reported comorbidities and medication was expressed by a four-level concordance score.
Results
Approaches I and II demonstrated that the patterns of concordance scores markedly differed between comorbidities in the COSYCONET data. On average, Approach I resulted in more than 50% concordance of all reported diseases to at least one medication. The more specific Approach II showed larger differences in the matching with medications, due to large differences in the disease-specificity of drugs. The highest concordance was achieved for diabetes and three combined cardiovascular disorders, while it was substantial for dyslipidemia and hyperuricemia, and low for asthma.
Conclusion
Both approaches represent feasible strategies to confirm self-reported diagnoses via medication. Approach I covers a broad spectrum of diseases and medications but is limited regarding disease-specificity. Approach II uses the information from medications specific for a single disease and therefore can reach higher concordance scores. The strategies described in a detailed and reproducible manner are generally applicable in large studies and might be useful to extract as much information as possible from the available data.

许可

CC BY 4.0

引用

学术使用请引用 DOI 10.1371/journal.pone.0163408（source: https://plos.figshare.com/articles/dataset/Systematic_Analysis_of_Self-Reported_Comorbidities_in_Large_Cohort_Studies_A_Novel_Stepwise_Approach_by_Evaluation_of_Medication/4128705）。