S1数据集

数据集获取（Figshare）

• 源站标题：S1 Dataset -
• DOI：10.1371/journal.pone.0303519.s002
• 发布日期：2024-05-09
• 文件数：1 个
• 文件总体积：343.44 KB
• 源站页面：https://plos.figshare.com/articles/dataset/S1_Dataset_-/25789974

命令行下载

curl -L -o "pone.0303519.s002.zip" "https://ndownloader.figshare.com/files/46222566"

文件清单

• pone.0303519.s002.zip（343.44 KB）

文件总体积 343.44 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
To establish whether or not a natural language processing technique could identify two common inpatient neurosurgical comorbidities using only text reports of inpatient head imaging.
Materials and methods
A training and testing dataset of reports of 979 CT or MRI scans of the brain for patients admitted to the neurosurgery service of a single hospital in June 2021 or to the Emergency Department between July 1–8, 2021, was identified. A variety of machine learning and deep learning algorithms utilizing natural language processing were trained on the training set (84% of the total cohort) and tested on the remaining images. A subset comparison cohort (n = 76) was then assessed to compare output of the best algorithm against real-life inpatient documentation.
Results
For “brain compression”, a random forest classifier outperformed other candidate algorithms with an accuracy of 0.81 and area under the curve of 0.90 in the testing dataset. For “brain edema”, a random forest classifier again outperformed other candidate algorithms with an accuracy of 0.92 and AUC of 0.94 in the testing dataset. In the provider comparison dataset, for “brain compression,” the random forest algorithm demonstrated better accuracy (0.76 vs 0.70) and sensitivity (0.73 vs 0.43) than provider documentation. For “brain edema,” the algorithm again demonstrated better accuracy (0.92 vs 0.84) and AUC (0.45 vs 0.09) than provider documentation.
Discussion
A natural language processing-based machine learning algorithm can reliably and reproducibly identify selected common neurosurgical comorbidities from radiology reports.
Conclusion
This result may justify the use of machine learning-based decision support to augment provider documentation.

许可

CC BY 4.0

引用

学术使用请引用 DOI 10.1371/journal.pone.0303519.s002（source: https://plos.figshare.com/articles/dataset/S1_Dataset_-/25789974）。