Table_2 出血转化预测的机器学习方法：捕捉预测因子交互作用

数据集获取（Figshare）

• 源站标题：Table_2_Machine learning approach for hemorrhagic transformation prediction: Capturing predictors’’ interaction.docx
• DOI：10.3389/fneur.2022.951401.s004
• 发布日期：2022-11-24
• 文件数：1 个
• 文件总体积：16.77 KB
• 源站页面：https://frontiersin.figshare.com/articles/dataset/Table_2_Machine_learning_approach_for_hemorrhagic_transformation_prediction_Capturing_predictors_interaction_docx/21615000

命令行下载

curl -L -o "Table_2_Machine learning approach for hemorrhagic transformation prediction: Capturing predictors'' interaction.docx" "https://ndownloader.figshare.com/files/38308365"

文件清单

• Table_2_Machine learning approach for hemorrhagic transformation prediction: Capturing predictors'' interaction.docx（16.77 KB）

文件总体积 16.77 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 purposePatients with ischemic stroke frequently develop hemorrhagic transformation (HT), which could potentially worsen the prognosis. The objectives of the current study were to determine the incidence and predictors of HT, to evaluate predictor interaction, and to identify the optimal predicting models.
MethodsA prospective study included 360 patients with ischemic stroke, of whom 354 successfully continued the study. Patients were subjected to thorough general and neurological examination and T2 diffusion-weighted MRI, at admission and 1 week later to determine the incidence of HT. HT predictors were selected by a filter-based minimum redundancy maximum relevance (mRMR) algorithm independent of model performance. Several machine learning algorithms including multivariable logistic regression classifier (LRC), support vector classifier (SVC), random forest classifier (RFC), gradient boosting classifier (GBC), and multilayer perceptron classifier (MLPC) were optimized for HT prediction in a randomly selected half of the sample (training set) and tested in the other half of the sample (testing set). The model predictive performance was evaluated using receiver operator characteristic (ROC) and visualized by observing case distribution relative to the models’’ predicted three-dimensional (3D) hypothesis spaces within the testing dataset true feature space. The interaction between predictors was investigated using generalized additive modeling (GAM).
ResultsThe incidence of HT in patients with ischemic stroke was 19.8%. Infarction size, cerebral microbleeds (CMB), and the National Institute of Health stroke scale (NIHSS) were identified as the best HT predictors. RFC (AUC: 0.91, 95% CI: 0.85–0.95) and GBC (AUC: 0.91, 95% CI: 0.86–0.95) demonstrated significantly superior performance compared to LRC (AUC: 0.85, 95% CI: 0.79–0.91) and MLPC (AUC: 0.85, 95% CI: 0.78–0.92). SVC (AUC: 0.90, 95% CI: 0.85–0.94) outperformed LRC and MLPC but did not reach statistical significance. LRC and MLPC did not show significant differences. The best models’’ 3D hypothesis spaces demonstrated non-linear decision boundaries suggesting an interaction between predictor vari

许可

CC BY 4.0

引用

学术使用请引用 DOI 10.3389/fneur.2022.951401.s004（source: https://frontiersin.figshare.com/articles/dataset/Table_2_Machine_learning_approach_for_hemorrhagic_transformation_prediction_Capturing_predictors_interaction_docx/21615000）。