使用常规收集的医疗数据识别帕金森病和帕金森综合征病例：系统综述

数据集获取（Figshare）

• 源站标题：Identifying Parkinson’'s disease and parkinsonism cases using routinely collected healthcare data: A systematic review
• DOI：10.1371/journal.pone.0198736
• 发布日期：2019-01-31
• 文件数：4 个
• 文件总体积：144.25 KB
• 源站页面：https://plos.figshare.com/articles/dataset/Identifying_Parkinson_s_disease_and_parkinsonism_cases_using_routinely_collected_healthcare_data_A_systematic_review/7656350

命令行下载

curl -L -o "pone.0198736.s001.doc" "https://ndownloader.figshare.com/files/14223230"
curl -L -o "pone.0198736.s002.docx" "https://ndownloader.figshare.com/files/14223233"
curl -L -o "pone.0198736.s003.docx" "https://ndownloader.figshare.com/files/14223236"
curl -L -o "pone.0198736.s004.docx" "https://ndownloader.figshare.com/files/14223239"

文件清单

• pone.0198736.s001.doc（65.54 KB）
• pone.0198736.s002.docx（17.37 KB）
• pone.0198736.s003.docx（30.89 KB）
• pone.0198736.s004.docx（30.45 KB）

文件总体积 144.25 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

Background
Population-based, prospective studies can provide important insights into Parkinson’s disease (PD) and other parkinsonian disorders. Participant follow-up in such studies is often achieved through linkage to routinely collected healthcare datasets. We systematically reviewed the published literature on the accuracy of these datasets for this purpose.
Methods
We searched four electronic databases for published studies that compared PD and parkinsonism cases identified using routinely collected data to a reference standard. We extracted study characteristics and two accuracy measures: positive predictive value (PPV) and/or sensitivity.
Results
We identified 18 articles, resulting in 27 measures of PPV and 14 of sensitivity. For PD, PPV ranged from 56–90% in hospital datasets, 53–87% in prescription datasets, 81–90% in primary care datasets and was 67% in mortality datasets. Combining diagnostic and medication codes increased PPV. For parkinsonism, PPV ranged from 36–88% in hospital datasets, 40–74% in prescription datasets, and was 94% in mortality datasets. Sensitivity ranged from 15–73% in single datasets for PD and 43–63% in single datasets for parkinsonism.
Conclusions
In many settings, routinely collected datasets generate good PPVs and reasonable sensitivities for identifying PD and parkinsonism cases. However, given the wide range of identified accuracy estimates, we recommend cohorts conduct their own context-specific validation studies if existing evidence is lacking. Further research is warranted to investigate primary care and medication datasets, and to develop algorithms that balance a high PPV with acceptable sensitivity.

许可

CC BY 4.0

引用

学术使用请引用 DOI 10.1371/journal.pone.0198736（source: https://plos.figshare.com/articles/dataset/Identifying_Parkinson_s_disease_and_parkinsonism_cases_using_routinely_collected_healthcare_data_A_systematic_review/7656350）。