基于紧急临床数据预测中风和创伤患者3天住院死亡率的机器学习模型数据集表1

数据集获取（Figshare）

• 源站标题：Table 1_A machine learning model based on emergency clinical data predicting 3-day in-hospital mortality for stroke and trauma patients.docx
• DOI：10.3389/fneur.2025.1512297.s001
• 发布日期：2025-03-20
• 文件数：1 个
• 文件总体积：19.10 KB
• 源站页面：https://frontiersin.figshare.com/articles/dataset/Table_1_A_machine_learning_model_based_on_emergency_clinical_data_predicting_3-day_in-hospital_mortality_for_stroke_and_trauma_patients_docx/28632095

命令行下载

curl -L -o "Table 1_A machine learning model based on emergency clinical data predicting 3-day in-hospital mortality for stroke and trauma patients.docx" "https://ndownloader.figshare.com/files/53127368"

文件清单

• Table 1_A machine learning model based on emergency clinical data predicting 3-day in-hospital mortality for stroke and trauma patients.docx（19.10 KB）

文件总体积 19.10 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

BackgroundAccurately predicting the short-term in-hospital mortality risk for patients with stroke and TBI (Traumatic Brain Injury) is crucial for improving the quality of emergency medical care.
MethodThis study analyzed data from 2,125 emergency admission patients with stroke and traumatic brain injury at two Grade a hospitals in China from January 2021 to March 2024. LASSO regression was used for feature selection, and the predictive performance of logistic regression was compared with six machine learning algorithms. A 70:30 ratio was applied for cross-validation, and confidence intervals were calculated using the bootstrap method. Temporal validation was performed on the best-performing model. SHAP values were employed to assess variable importance.
ResultsThe random forest algorithm excelled in predicting in-hospital 3-day mortality, achieving an AUC of 0.978 (95% CI: 0.966–0.986). Time series validation demonstrated the model’s strong generalization capability, with an AUC of 0.975 (95% CI: 0.963–0.986). Key predictive factors in the final model included metabolic syndrome, NEWS2 score, Glasgow Coma Scale (GCS), whether surgery was performed, bowel movement status, potassium level (K), aspartate transaminase (AST) level, and temporal factors. SHAP value analysis further confirmed the significant contributions of these variables to the predictive outcomes. The random forest model developed in this study demonstrates good accuracy in predicting short-term in-hospital mortality rates for stroke and traumatic brain injury patients. The model integrates emergency scores, clinical signs, and key biochemical indicators, providing a comprehensive perspective for risk assessment. This approach, which incorporates emergency data, holds promise for assisting decision-making in clinical practice, thereby improving patient outcomes.

许可

CC BY 4.0

引用

学术使用请引用 DOI 10.3389/fneur.2025.1512297.s001（source: https://frontiersin.figshare.com/articles/dataset/Table_1_A_machine_learning_model_based_on_emergency_clinical_data_predicting_3-day_in-hospital_mortality_for_stroke_and_trauma_patients_docx/28632095）。