严重烧伤患者脓毒症和死亡率可解释预测模型数据集（Table 1）

数据集获取（Figshare）

• 源站标题：Table 1_Development and validation of web-based, interpretable predictive models for sepsis and mortality in extensive burns.docx
• DOI：10.3389/fcimb.2025.1586087.s003
• 发布日期：2025-08-18
• 文件数：1 个
• 文件总体积：20.79 KB
• 源站页面：https://frontiersin.figshare.com/articles/dataset/Table_1_Development_and_validation_of_web-based_interpretable_predictive_models_for_sepsis_and_mortality_in_extensive_burns_docx/29929247

命令行下载

curl -L -o "Table 1_Development and validation of web-based, interpretable predictive models for sepsis and mortality in extensive burns.docx" "https://ndownloader.figshare.com/files/57249491"

文件清单

• Table 1_Development and validation of web-based, interpretable predictive models for sepsis and mortality in extensive burns.docx（20.79 KB）

文件总体积 20.79 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

BackgroundBurn injuries are a common cause of trauma globally, with extensive burns (≥ 50% total body surface area burned) associated with high rates of sepsis and mortality. This study aims to identify risk factors associated with sepsis and mortality in extensively burned patients and to develop accurate, interpretable predictive models via machine learning algorithms.
MethodsA retrospective cohort study was conducted utilizing data from two Burn Critical Care Units in Eastern China from 2012-2023. A total of 237 patients with extensive burns were included. We applied ten machine learning algorithms, including random forest, gradient boosting tree (GBT), and logistic regression, to predict sepsis and mortality. The models were evaluated via AUC, precision, recall, accuracy, and F1 score, and were compared with the SOFA score performance. Model interpretability was enhanced via SHapley Additive exPlanations (SHAP).
ResultsThe key predictive factors for sepsis included the SOFA score, new onset shock, albumin, blood urea nitrogen (BUN), third-degree burned area, TBSA burned, white blood cell count, and inhalation injury. For mortality, the key predictive factors included alanine aminotransferase (ALT), the SOFA score, type of burn, new onset shock, third-degree burn area, TBSA burned, and sepsis. The RF model demonstrated superior performance in predicting sepsis (AUC = 0.977, accuracy = 0.945, recall = 0.964, precision = 0.930, and F1 score = 0.945). For mortality prediction, the GBT model yielded the highest AUC of 0.981 (accuracy = 0.952, recall = 0.965, precision = 0.942, and F1 score = 0.953). The sepsis prediction model outperformed the SOFA-based logistic regression model. Web-based calculators were developed to aid clinical decision-making.
ConclusionMachine learning models, RF and GBT, demonstrate strong predictive ability for sepsis and mortality in extensive burn patients. The application of SHAP enhances model transparency, facilitating clinical interpretation and early intervention. Two web-based calculators can guide intensive care strategies and improve patient outcomes.

许可

CC BY 4.0

引用

学术使用请引用 DOI 10.3389/fcimb.2025.1586087.s003（source: https://frontiersin.figshare.com/articles/dataset/Table_1_Development_and_validation_of_web-based_interpretable_predictive_models_for_sepsis_and_mortality_in_extensive_burns_docx/29929247）。