基于临床数据的慢性阻塞性肺疾病家庭康复管理智能诊断与治疗模型

数据集获取（Figshare）

• 源站标题：Intelligent Diagnosis and Treatment Model Based on Clinical Data for Home Rehabilitation Management of Chronic Obstructive Pulmonary Disease
• DOI：10.6084/m9.figshare.30145807.v3
• 发布日期：2025-09-17
• 文件数：1 个
• 文件总体积：1.41 MB
• 源站页面：https://figshare.com/articles/dataset/Intelligent_Diagnosis_and_Treatment_Model_Based_on_Clinical_Data_for_Home_Rehabilitation_Management_of_Chronic_Obstructive_Pulmonary_Disease/30145807

命令行下载

curl -L -o "Supplementary_Data_COPD_Study.zip" "https://ndownloader.figshare.com/files/58028164"

文件清单

• Supplementary_Data_COPD_Study.zip（1.41 MB）

文件总体积 1.41 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

Traditional in-hospital rehabilitation treatment for chronic obstructive pulmonary disease (COPD) has problems such as resource shortage, high cost, and inconvenient transportation. To improve the convenience of rehabilitation treatment for COPD, a clinical data-driven intelligent diagnosis and treatment model is proposed for home rehabilitation management of COPD. The intelligent diagnosis and treatment model for home rehabilitation of COPD is optimized by combining the XGBoost algorithm and the long short-term memory network. The outcomes indicate that the XGBoost algorithm has the highest accuracy in processing clinical structured data, while the random forest (RF) algorithm has the lowest accuracy in processing clinical structured data. When the quantity of training samples is 300, the accuracy rates are 98% and 83%, respectively. The integration of the XGBoost algorithm and the long short-term memory network is used to process the monitoring indicators, resulting in the highest improvement rate and the smallest mean square error. When the sample size is 1000, the improvement rate of monitoring indicators is 42.5%, and the mean square error is 0.041. The method proposed in the study can effectively process clinical data, improve the accuracy of data processing, and accurately predict future changes in COPD.

许可

CC BY 4.0

引用

学术使用请引用 DOI 10.6084/m9.figshare.30145807.v3（source: https://figshare.com/articles/dataset/Intelligent_Diagnosis_and_Treatment_Model_Based_on_Clinical_Data_for_Home_Rehabilitation_Management_of_Chronic_Obstructive_Pulmonary_Disease/30145807）。