机器学习预测大血管闭塞无效再通表1

数据集获取（Figshare）

• 源站标题：Table_1_Machine learning to predict futile recanalization of large vessel occlusion before and after endovascular thrombectomy.XLSX
• DOI：10.3389/fneur.2022.909403.s002
• 发布日期：2022-08-19
• 文件数：1 个
• 文件总体积：101.13 KB
• 源站页面：https://frontiersin.figshare.com/articles/dataset/Table_1_Machine_learning_to_predict_futile_recanalization_of_large_vessel_occlusion_before_and_after_endovascular_thrombectomy_XLSX/20516229

命令行下载

curl -L -o "Table_1_Machine learning to predict futile recanalization of large vessel occlusion before and after endovascular thrombectomy.XLSX" "https://ndownloader.figshare.com/files/36721356"

文件清单

• Table_1_Machine learning to predict futile recanalization of large vessel occlusion before and after endovascular thrombectomy.XLSX（101.13 KB）

文件总体积 101.13 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

Background and purposeFutile recanalization occurs when the endovascular thrombectomy (EVT) is a technical success but fails to achieve a favorable outcome. This study aimed to use machine learning (ML) algorithms to develop a pre-EVT model and a post-EVT model to predict the risk of futile recanalization and to provide meaningful insights to assess the prognostic factors associated with futile recanalization.
MethodsConsecutive acute ischemic stroke patients with large vessel occlusion (LVO) undergoing EVT at the National Advanced Stroke Center of Nanjing First Hospital (China) between April 2017 and May 2021 were analyzed. The baseline characteristics and peri-interventional characteristics were assessed using four ML algorithms. The predictive performance was evaluated by the area under curve (AUC) of receiver operating characteristic and calibration curve. In addition, the SHapley Additive exPlanations (SHAP) approach and partial dependence plot were introduced to understand the relative importance and the influence of a single feature.
ResultsA total of 312 patients were included in this study. Of the four ML models that include baseline characteristics, the “Early” XGBoost had a better performance {AUC, 0.790 [95% confidence intervals (CI), 0.677–0.903]; Brier, 0.191}. Subsequent inclusion of peri-interventional characteristics into the “Early” XGBoost showed that the “Late” XGBoost performed better [AUC, 0.910 (95% CI, 0.837–0.984); Brier, 0.123]. NIHSS after 24 h, age, groin to recanalization, and the number of passages were the critical prognostic factors associated with futile recanalization, and the SHAP approach shows that NIHSS after 24 h ranks first in relative importance.
ConclusionsThe “Early” XGBoost and the “Late” XGBoost allowed us to predict futile recanalization before and after EVT accurately. Our study suggests that including peri-interventional characteristics may lead to superior predictive performance compared to a model based on baseline characteristics only. In addition, NIHSS after 24 h was the most important prognostic factor for futile recanalization.

许可

CC BY 4.0

引用

学术使用请引用 DOI 10.3389/fneur.2022.909403.s002（source: https://frontiersin.figshare.com/articles/dataset/Table_1_Machine_learning_to_predict_futile_recanalization_of_large_vessel_occlusion_before_and_after_endovascular_thrombectomy_XLSX/20516229）。