数据表1：开发机器学习算法预测急诊胃肠道手术后肺部并发症（PDF）

数据集获取（Figshare）

• 源站标题：Data_Sheet_1_Developing Machine Learning Algorithms to Predict Pulmonary Complications After Emergency Gastrointestinal Surgery.PDF
• DOI：10.3389/fmed.2021.655686.s001
• 发布日期：2021-08-02
• 文件数：1 个
• 文件总体积：689.99 KB
• 源站页面：https://frontiersin.figshare.com/articles/dataset/Data_Sheet_1_Developing_Machine_Learning_Algorithms_to_Predict_Pulmonary_Complications_After_Emergency_Gastrointestinal_Surgery_PDF/15088872

命令行下载

curl -L -o "Data_Sheet_1_Developing Machine Learning Algorithms to Predict Pulmonary Complications After Emergency Gastrointestinal Surgery.PDF" "https://ndownloader.figshare.com/files/29010630"

文件清单

• Data_Sheet_1_Developing Machine Learning Algorithms to Predict Pulmonary Complications After Emergency Gastrointestinal Surgery.PDF（689.99 KB）

文件总体积 689.99 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: Investigate whether machine learning can predict pulmonary complications (PPCs) after emergency gastrointestinal surgery in patients with acute diffuse peritonitis.
Methods: This is a secondary data analysis study. We use five machine learning algorithms (Logistic regression, DecisionTree, GradientBoosting, Xgbc, and gbm) to predict postoperative pulmonary complications.
Results: Nine hundred and twenty-six cases were included in this study; 187 cases (20.19%) had PPCs. The five most important variables for the postoperative weight were preoperative albumin, cholesterol on the 3rd day after surgery, albumin on the day of surgery, platelet count on the 1st day after surgery and cholesterol count on the 1st day after surgery for pulmonary complications. In the test group: the logistic regression model shows AUC = 0.808, accuracy = 0.824 and precision = 0.621; Decision tree shows AUC = 0.702, accuracy = 0.795 and precision = 0.486; The GradientBoosting model shows AUC = 0.788, accuracy = 0.827 and precision = 1.000; The Xgbc model shows AUC = 0.784, accuracy = 0.806 and precision = 0.583. The Gbm model shows AUC = 0.814, accuracy = 0.806 and precision = 0.750.
Conclusion: Machine learning algorithms can predict patients’’ PPCs with acute diffuse peritonitis. Moreover, the results of the importance matrix for the Gbdt algorithm model show that albumin, cholesterol, age, and platelets are the main variables that account for the highest pulmonary complication weights.

许可

CC BY 4.0

引用

学术使用请引用 DOI 10.3389/fmed.2021.655686.s001（source: https://frontiersin.figshare.com/articles/dataset/Data_Sheet_1_Developing_Machine_Learning_Algorithms_to_Predict_Pulmonary_Complications_After_Emergency_Gastrointestinal_Surgery_PDF/15088872）。