冠心病监护病房患者预后预测数据表

数据集获取（Figshare）

• 源站标题：Data_Sheet_1_Predicting the Prognosis of Patients in the Coronary Care Unit: A Novel Multi-Category Machine Learning Model Using XGBoost.zip
• DOI：10.3389/fcvm.2022.764629.s001
• 发布日期：2022-05-12
• 文件数：1 个
• 文件总体积：9.34 MB
• 源站页面：https://frontiersin.figshare.com/articles/dataset/Data_Sheet_1_Predicting_the_Prognosis_of_Patients_in_the_Coronary_Care_Unit_A_Novel_Multi-Category_Machine_Learning_Model_Using_XGBoost_zip/19751044

命令行下载

curl -L -o "Data_Sheet_1_Predicting the Prognosis of Patients in the Coronary Care Unit: A Novel Multi-Category Machine Learning Model Using XGBoost.zip" "https://ndownloader.figshare.com/files/35094817"

文件清单

• Data_Sheet_1_Predicting the Prognosis of Patients in the Coronary Care Unit: A Novel Multi-Category Machine Learning Model Using XGBoost.zip（9.34 MB）

文件总体积 9.34 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

BackgroundEarly prediction and classification of prognosis is essential for patients in the coronary care unit (CCU). We applied a machine learning (ML) model using the eXtreme Gradient Boosting (XGBoost) algorithm to prognosticate CCU patients and compared XGBoost with traditional classification models.
MethodsCCU patients’’ data were extracted from the MIMIC-III v1.4 clinical database, and divided into four groups based on the time to death: Overall, 5360 CCU patients were included. Compared to NB, LR, and SVM, the XGBoost model showed better accuracy (0.663, 0.605, 0.632, and 0.622), micro-AUCs (0.873, 0.811, 0.841, and 0.818), and MCC (0.337, 0.317, 0.250, and 0.182). In subgroup analysis, the XGBoost model had a better predictive performance in acute myocardial infarction subgroup. The decision curve and clinical impact curve analyses verified the clinical utility of the XGBoost model for different categories of patients. Finally, we obtained a simplified model with thirty features.
ConclusionsFor CCU physicians, the ML technique by XGBoost is a potential predictive tool in patients with different conditions, and it may contribute to improvements in prognosis.

许可

CC BY 4.0

引用

学术使用请引用 DOI 10.3389/fcvm.2022.764629.s001（source: https://frontiersin.figshare.com/articles/dataset/Data_Sheet_1_Predicting_the_Prognosis_of_Patients_in_the_Coronary_Care_Unit_A_Novel_Multi-Category_Machine_Learning_Model_Using_XGBoost_zip/19751044）。