慢性阻塞性肺疾病患者分诊的机器学习方法

数据集获取（Figshare）

• 源站标题：A machine learning approach to triaging patients with chronic obstructive pulmonary disease
• DOI：10.1371/journal.pone.0188532
• 发布日期：2017-11-22
• 文件数：4 个
• 文件总体积：4.55 MB
• 源站页面：https://plos.figshare.com/articles/dataset/A_machine_learning_approach_to_triaging_patients_with_chronic_obstructive_pulmonary_disease/5626249

命令行下载

curl -L -o "S1 Table.xlsx" "https://ndownloader.figshare.com/files/9799945"
curl -L -o "S1 Document.docx" "https://ndownloader.figshare.com/files/9799948"
curl -L -o "S1 Spreadsheet.xlsx" "https://ndownloader.figshare.com/files/9799951"
curl -L -o "S1 Dataset.zip" "https://ndownloader.figshare.com/files/9799954"

文件清单

• S1 Table.xlsx（32.60 KB）
• S1 Document.docx（19.04 KB）
• S1 Spreadsheet.xlsx（203.40 KB）
• S1 Dataset.zip（4.30 MB）

文件总体积 4.55 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

COPD patients are burdened with a daily risk of acute exacerbation and loss of control, which could be mitigated by effective, on-demand decision support tools. In this study, we present a machine learning-based strategy for early detection of exacerbations and subsequent triage. Our application uses physician opinion in a statistically and clinically comprehensive set of patient cases to train a supervised prediction algorithm. The accuracy of the model is assessed against a panel of physicians each triaging identical cases in a representative patient validation set. Our results show that algorithm accuracy and safety indicators surpass all individual pulmonologists in both identifying exacerbations and predicting the consensus triage in a 101 case validation set. The algorithm is also the top performer in sensitivity, specificity, and ppv when predicting a patient’s need for emergency care.

许可

CC BY 4.0

引用

学术使用请引用 DOI 10.1371/journal.pone.0188532（source: https://plos.figshare.com/articles/dataset/A_machine_learning_approach_to_triaging_patients_with_chronic_obstructive_pulmonary_disease/5626249）。