心源性休克结局预测机器学习表4

数据集获取（Figshare）

• 源站标题：Table_4_Machine Learning for Prediction of Outcomes in Cardiogenic Shock.DOCX
• DOI：10.3389/fcvm.2022.849688.s004
• 发布日期：2022-05-06
• 文件数：1 个
• 文件总体积：19.12 KB
• 源站页面：https://frontiersin.figshare.com/articles/dataset/Table_4_Machine_Learning_for_Prediction_of_Outcomes_in_Cardiogenic_Shock_DOCX/19720498

命令行下载

curl -L -o "Table_4_Machine Learning for Prediction of Outcomes in Cardiogenic Shock.DOCX" "https://ndownloader.figshare.com/files/35023126"

文件清单

• Table_4_Machine Learning for Prediction of Outcomes in Cardiogenic Shock.DOCX（19.12 KB）

文件总体积 19.12 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

ObjectiveThe management of cardiogenic shock (CS) in the elderly remains a major clinical challenge. Existing clinical prediction models have not performed well in assessing the prognosis of elderly patients with CS. This study aims to build a predictive model, which could better predict the 30-day mortality of elderly patients with CS.
MethodsWe extracted data from the Medical Information Mart for Intensive Care III version 1.4 (MIMIC-III) as the training set and the data of validation sets were collected from the Second Affiliated Hospital and Yuying Children’'s Hospital of Wenzhou Medical University. Three models, including the cox regression model, the Least Absolute Shrinkage and Selection Operator (LASSO) regression model, and the CoxBoost model, were established using the training set. Through the comparison of area under the receiver operating characteristic (ROC) curve (AUC), C index, net reclassification improvement (NRI), integrated discrimination improvement (IDI), and median improvement in risk score, the best model was selected. Then for external validation, compared the best model with the simplified acute physiology score II (SAPSII) and the CardShock risk score.
ResultsA total of 919 patients were included in the study, of which 804 patients were in the training set and 115 patients were in the verification set. Using the training set, we built three models: the cox regression model including 6 predictors, the LASSO regression model including 4 predictors, and the CoxBoost model including 16 predictors. Among them, the CoxBoost model had good discrimination [AUC: 0.730; C index: 0.6958 (0.6657, 0.7259)]. Compared with the CoxBoost model, the NRI, IDI, and median improvement in risk score of other models were allIn conclusion, this study showed that in predicting the 30-day mortality of elderly CS patients, the CoxBoost model was superior to the Cox regression model, LASSO regression model, SAPS II, and the CardShock risk score.

许可

CC BY 4.0

引用

学术使用请引用 DOI 10.3389/fcvm.2022.849688.s004（source: https://frontiersin.figshare.com/articles/dataset/Table_4_Machine_Learning_for_Prediction_of_Outcomes_in_Cardiogenic_Shock_DOCX/19720498）。