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

资源简介

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

【数据集背景】

本数据集源站标题为《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》。配套论文《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》发表于 Neurological Research（2025），DOI：10.1080/01616412.2025.2592911。项目资助方：This study was supported by the Key Research and Development Program of the Ningxia Hui Autonomous Region (Project No. 2022BEG03165).。源站首次发布：2025-11-25。

【数据内容】

源站原始描述：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 algo…

【数据结构与技术规格】

文件数 4 个，合计 845.33 KB。yner_a_2592911_sm7375.docx：application/vnd.openxmlformats-officedocument.wordprocessingml.document，13.08 KB。yner_a_2592911_sm7374.docx：application/vnd.openxmlformats-officedocument.wordprocessingml.document，12.18 KB。yner_a_2592911_sm7373.pdf：application/pdf，795.73 KB。数据集 DOI：10.6084/m9.figshare.30715733.v1。

【主题与分类】

源站学科分类：Medicine、Immunology、Biological Sciences not elsewhere classified、Plant Biology。主题标签：Aneurysmal subarachnoid haemorrhage、mortality prediction、Boruta、LASSO、SMOTE。

【适用方向】

结构化表格数据可直接用于统计建模、特征工程与队列分析。

【获取与许可】

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

使用许可：CC BY 4.0（https://creativecommons.org/licenses/by/4.0/）。

规范引用：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. Taylor & Francis. Ma, LongXiang; Zhang, Bin; Li, Xiangxin; Yu, Yinhui; Zou, Jierong; Yi, Chaohui; et al