数据表2：结合电子健康记录与集成机器学习方法预测子宫内膜癌

数据集获取（Figshare）

• 源站标题：Data_Sheet_2_Prediction of Endometrial Carcinoma Using the Combination of Electronic Health Records and an Ensemble Machine Learning Method.docx
• DOI：10.3389/fmed.2022.851890.s002
• 发布日期：2022-03-04
• 文件数：1 个
• 文件总体积：506.84 KB
• 源站页面：https://frontiersin.figshare.com/articles/dataset/Data_Sheet_2_Prediction_of_Endometrial_Carcinoma_Using_the_Combination_of_Electronic_Health_Records_and_an_Ensemble_Machine_Learning_Method_docx/19305791

命令行下载

curl -L -o "Data_Sheet_2_Prediction of Endometrial Carcinoma Using the Combination of Electronic Health Records and an Ensemble Machine Learning Method.docx" "https://ndownloader.figshare.com/files/34290698"

文件清单

• Data_Sheet_2_Prediction of Endometrial Carcinoma Using the Combination of Electronic Health Records and an Ensemble Machine Learning Method.docx（506.84 KB）

文件总体积 506.84 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

Endometrial carcinoma (EC) is a common cause of cancer death in women, and having an early accurate prediction model to identify this disease is crucial. The aim of this study was to develop a new machine learning (ML) model-based diagnostic prediction model for EC. We collected data from consecutive patients between November 2012 and January 2021 at tertiary hospitals in central China. Inclusion criteria included women undergoing endometrial biopsy, dilation and curettage, or hysterectomy. A total of 9 features, including patient demographics, vital signs, and laboratory and ultrasound results, were selected in the final analysis. This new model was combined with three top optimal ML methods, namely, logistic regression, gradient-boosted decision tree, and random forest. A total of 1,922 patients were eligible for final analysis and modeling. The ensemble model, called TJHPEC, was validated in an internal validation cohort and two external validation cohorts. The results showed that the AUC values were 0.9346, 0.8341, and 0.8649 for the prediction of total EC and 0.9347, 0.8073, and 0.871 for prediction of stage I EC. Nine clinical features were confirmed to be highly related to the prediction of EC in TJHPEC. In conclusion, our new model may be accurate for identifying EC, especially in the early stage, in the general population of central China.

许可

CC BY 4.0

引用

学术使用请引用 DOI 10.3389/fmed.2022.851890.s002（source: https://frontiersin.figshare.com/articles/dataset/Data_Sheet_2_Prediction_of_Endometrial_Carcinoma_Using_the_Combination_of_Electronic_Health_Records_and_an_Ensemble_Machine_Learning_Method_docx/19305791）。