动脉瘤性蛛网膜下腔出血血管内治疗后出院1年全因死亡率预测模型数据集（LASSO-Boruta特征选择）

数据集获取（Figshare）

• 源站标题：Development of a pre-discharge model for 1-year post-discharge all-cause mortality after endovascular treatment for aneurysmal subarachnoid haemorrhage using LASSO–Boruta feature selection
• DOI：10.6084/m9.figshare.30715733.v1
• 发布日期：2025-11-25
• 文件数：4 个
• 文件总体积：845.33 KB
• 源站页面：https://tandf.figshare.com/articles/dataset/Development_of_a_pre-discharge_model_for_1-year_post-discharge_all-cause_mortality_after_endovascular_treatment_for_aneurysmal_subarachnoid_haemorrhage_using_LASSO_Boruta_feature_selection/30715733

命令行下载

curl -L -o "yner_a_2592911_sm7375.docx" "https://ndownloader.figshare.com/files/59844743"
curl -L -o "yner_a_2592911_sm7374.docx" "https://ndownloader.figshare.com/files/59844746"
curl -L -o "yner_a_2592911_sm7373.pdf" "https://ndownloader.figshare.com/files/59844749"
curl -L -o "yner_a_2592911_sm7372.docx" "https://ndownloader.figshare.com/files/59844752"

文件清单

• yner_a_2592911_sm7375.docx（13.08 KB）
• yner_a_2592911_sm7374.docx（12.18 KB）
• yner_a_2592911_sm7373.pdf（795.73 KB）
• yner_a_2592911_sm7372.docx（24.34 KB）

文件总体积 845.33 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

To develop a predischarge model for predicting 1-year post-discharge all-cause mortality in patients with aneurysmal subarachnoid haemorrhage (aSAH) following endovascular treatment (EVT).
We retrospectively analysed 947 patients with aSAH who were discharged alive between April 2021 and April 2023 from four neurointerventional centres in China as the training cohort. Candidate variables were selected using the least absolute shrinkage and selection operator (LASSO) combined with the Boruta algorithm. Based on these features, six models – logistic regression (LR), XGBoost, random forest (RF), AdaBoost, decision tree, and gradient boosting decision tree (GBDT) – were developed and compared. The optimal model was selected by the area under the receiver operating characteristic curve (AUC). The external validation cohort comprised 692 aSAH patients discharged alive between April 2023 and April 2024 from two additional centres. Model performance was evaluated using AUC, calibration curves, and decision curve analysis (DCA). Given the imbalanced outcome distribution, we applied the Synthetic Minority Over-sampling Technique (SMOTE) to further assess model generalisability.
Among 1,639 patients alive at discharge, 67 (4.1%) died within 1 year. LASSO and Boruta jointly identified five key predictors for model construction: age, modified World Federation of Neurosurgical Societies (mWFNS) grade, ICU length of stay (ICU-LOS), C-reactive protein (CRP), and monocyte-to-HDL ratio (MHR). The random forest achieved the best discrimination in training set and remained strong in external validation cohorts.Moreover, SMOTE training yielded further improvements in generalisability.
Random forest model enables individualised pre-discharge risk stratification and may guide perioperative management.

许可

CC BY 4.0

引用

学术使用请引用 DOI 10.6084/m9.figshare.30715733.v1（source: https://tandf.figshare.com/articles/dataset/Development_of_a_pre-discharge_model_for_1-year_post-discharge_all-cause_mortality_after_endovascular_treatment_for_aneurysmal_subarachnoid_haemorrhage_using_LASSO_Boruta_feature_selection/30715733）。