慢性阻塞性肺疾病(COPD)患者预先护理计划中的决策过程
COPD患者预先护理计划决策过程的研究数据集。

基本信息
资源简介
慢性阻塞性肺疾病(COPD)患者预先护理计划中的决策过程
【数据集背景】
本数据集源站标题为《The decision-making processes in advance care planning for people with Chronic Obstructive Disease (COPD)》。项目资助方:N/A。源站首次发布:2024-04-23。
【数据内容】
源站原始描述:People with Chronic Obstructive Pulmonary Disease live with a progressive illness with unpredictable trajectory. Advance Care Planning may be of value in meeting their future needs according to their preferences, beliefs, and values
【数据结构与技术规格】
文件数 9 个,合计 4.49 MB。Academic Search Complete Search History .pdf:application/pdf,372.35 KB。Apa PsycInfo Search History.pdf:application/pdf,302.40 KB。CINAHL SEARCH Search History.pdf:application/pdf,380.13 KB。数据集 DOI:10.6084/m9.figshare.23247917.v2。
【主题与分类】
源站学科分类:Health services and systems not elsewhere classified。主题标签:Advance Care Planning、Chronic Obstructive Pulmonary Disease、Decision-making、Palliative Care、Conversations。
【适用方向】
文本类数据可用于医学自然语言处理,如命名实体识别、信息抽取与文本分类。
【获取与许可】
源站页面:https://figshare.com/articles/dataset/_b_The_decision-making_processes_in_advance_care_planning_for_people_with_Chronic_Obstructive_Disease_COPD_b_/23247917
使用许可:CC BY 4.0(https://creativecommons.org/licenses/by/4.0/)。
规范引用:Zuka, Margaret; Doody, Owen; Moloney, Mairead; Cassidy, Irene (2024). The decision-making processes in advance care planning for people with Chronic Obstructive Disease (COPD). figshare. Dataset
下载信息
注册下载
需要注册 Kaggle 账号并登录后下载,适合需要跟踪下载记录和使用 API 的用户。
暂未开放公开下载
无需注册即可直接获取公开样本或文档,适合快速预览和评估数据集质量。
免登录有偿下载
公开数据集受托下载与技术交付服务。
提供高速下载与技术交付服务(收技术服务费,非数据销售)
暂未开放当前数据集主要来源为 Kaggle 公开托管,完整影像包建议通过原始链接或 Kaggle API 获取。
使用方式
数据集获取(Figshare)
- 源站标题:<b>The decision-making processes in advance care planning for people with Chronic Obstructive Disease (COPD)</b>
- DOI:10.6084/m9.figshare.23247917.v2
- 发布日期:2024-04-23
- 文件数:9 个
- 文件总体积:4.49 MB
- 源站页面:https://figshare.com/articles/dataset/_b_The_decision-making_processes_in_advance_care_planning_for_people_with_Chronic_Obstructive_Disease_COPD_b_/23247917
命令行下载
curl -L -o "Academic Search Complete Search History .pdf" "https://ndownloader.figshare.com/files/45809175"
curl -L -o "Apa PsycInfo Search History.pdf" "https://ndownloader.figshare.com/files/45809178"
curl -L -o "CINAHL SEARCH Search History.pdf" "https://ndownloader.figshare.com/files/45809181"
curl -L -o "Cochrane Search History.txt" "https://ndownloader.figshare.com/files/45809184"
curl -L -o "Embase Search History.pdf" "https://ndownloader.figshare.com/files/45809187"
curl -L -o "MEDLINE Search History.pdf" "https://ndownloader.figshare.com/files/45809190"
curl -L -o "Scopus Search History.pdf" "https://ndownloader.figshare.com/files/45809193"
curl -L -o "Web Of Science Search History.pdf" "https://ndownloader.figshare.com/files/45809196"
文件清单
Academic Search Complete Search History .pdf(372.35 KB)Apa PsycInfo Search History.pdf(302.40 KB)CINAHL SEARCH Search History.pdf(380.13 KB)Cochrane Search History.txt(1.53 KB)Embase Search History.pdf(2.85 MB)MEDLINE Search History.pdf(311.24 KB)Scopus Search History.pdf(189.90 KB)Web Of Science Search History.pdf(10.18 KB)Data analysis coding.xlsx(66.36 KB)
文件总体积 4.49 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
People with Chronic Obstructive Pulmonary Disease live with a progressive illness with unpredictable trajectory. Advance Care Planning may be of value in meeting their future needs according to their preferences, beliefs, and values.
许可
CC BY 4.0
引用
学术使用请引用 DOI 10.6084/m9.figshare.23247917.v2(source: https://figshare.com/articles/dataset/_b_The_decision-making_processes_in_advance_care_planning_for_people_with_Chronic_Obstructive_Disease_COPD_b_/23247917)。
数据缺失?
依托陕西、四川两大基地,我们与超过十家三甲医院建立直接合作关系,覆盖合规授权、采集治理、专业标注、数据交付的全流程,为AI医疗团队提供即拿即用的高质量临床数据。
⚡️ 需要数据支持或标注服务?立即联系我们获取专业方案。




