数据表1：传统逻辑回归与机器学习预测成人脓毒症患者死亡率的比较

数据集获取（Figshare）

• 源站标题：Data Sheet 1_Comparison between traditional logistic regression and machine learning for predicting mortality in adult sepsis patients.docx
• DOI：10.3389/fmed.2024.1496869.s001
• 发布日期：2025-01-06
• 文件数：1 个
• 文件总体积：295.86 KB
• 源站页面：https://frontiersin.figshare.com/articles/dataset/Data_Sheet_1_Comparison_between_traditional_logistic_regression_and_machine_learning_for_predicting_mortality_in_adult_sepsis_patients_docx/28141310

命令行下载

curl -L -o "Data Sheet 1_Comparison between traditional logistic regression and machine learning for predicting mortality in adult sepsis patients.docx" "https://ndownloader.figshare.com/files/51494090"

文件清单

• Data Sheet 1_Comparison between traditional logistic regression and machine learning for predicting mortality in adult sepsis patients.docx（295.86 KB）

文件总体积 295.86 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

BackgroundSepsis is a life-threatening disease associated with a high mortality rate, emphasizing the need for the exploration of novel models to predict the prognosis of this patient population. This study compared the performance of traditional logistic regression and machine learning models in predicting adult sepsis mortality.
ObjectiveTo develop an optimum model for predicting the mortality of adult sepsis patients based on comparing traditional logistic regression and machine learning methodology.
MethodsRetrospective analysis was conducted on 606 adult sepsis inpatients at our medical center between January 2020 and December 2022, who were randomly divided into training and validation sets in a 7:3 ratio. Traditional logistic regression and machine learning methods were employed to assess the predictive ability of mortality in adult sepsis. Univariate analysis identified independent risk factors for the logistic regression model, while Least Absolute Shrinkage and Selection Operator (LASSO) regression facilitated variable shrinkage and selection for the machine learning model. Among various machine learning models, which included Bagged Tree, Boost Tree, Decision Tree, LightGBM, Naïve Bayes, Nearest Neighbors, Support Vector Machine (SVM), and Random Forest (RF), the one with the maximum area under the curve (AUC) was chosen for model construction. Model validation and comparison with the Sequential Organ Failure Assessment (SOFA) and the Acute Physiology and Chronic Health Evaluation (APACHE) scores were performed using receiver operating characteristic (ROC) curves, calibration curves, and decision curve analysis (DCA) curves in the validation set.
ResultsUnivariate analysis was employed to assess 17 variables, namely gender, history of coronary heart disease (CHD), systolic pressure, white blood cell (WBC), neutrophil count (NEUT), lymphocyte count (LYMP), lactic acid, neutrophil-to-lymphocyte ratio (NLR), red blood cell distribution width (RDW), interleukin-6 (IL-6), prothrombin time (PT), international normalized ratio (INR), fibrinogen (FBI), D-dimer, aspartate aminotransferase (AST), total bilirubin (Tbil), and lung infection. Significant differenc

许可

CC BY 4.0

引用

学术使用请引用 DOI 10.3389/fmed.2024.1496869.s001（source: https://frontiersin.figshare.com/articles/dataset/Data_Sheet_1_Comparison_between_traditional_logistic_regression_and_machine_learning_for_predicting_mortality_in_adult_sepsis_patients_docx/28141310）。