静脉溶栓后卒中患者长期结局预测的可解释机器学习数据表1

数据集获取（Figshare）

• 源站标题：Data_Sheet_1_Explainable machine learning for long-term outcome prediction in two-center stroke patients after intravenous thrombolysis.docx
• DOI：10.3389/fnins.2023.1146197.s001
• 发布日期：2023-02-22
• 文件数：1 个
• 文件总体积：16.37 MB
• 源站页面：https://frontiersin.figshare.com/articles/dataset/Data_Sheet_1_Explainable_machine_learning_for_long-term_outcome_prediction_in_two-center_stroke_patients_after_intravenous_thrombolysis_docx/22138913

命令行下载

curl -L -o "Data_Sheet_1_Explainable machine learning for long-term outcome prediction in two-center stroke patients after intravenous thrombolysis.docx" "https://ndownloader.figshare.com/files/39358526"

文件清单

• Data_Sheet_1_Explainable machine learning for long-term outcome prediction in two-center stroke patients after intravenous thrombolysis.docx（16.37 MB）

文件总体积 16.37 MB（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

ObjectiveNeurological outcome prediction in patients with ischemic stroke is very critical in treatment strategy and post-stroke management. Machine learning techniques with high accuracy are increasingly being developed in the medical field. We studied the application of machine learning models to predict long-term neurological outcomes in patients with after intravenous thrombolysis.
MethodsA retrospective cohort study was performed to review all stroke patients with intravenous thrombolysis. Patients with modified Rankin Score (mRs) less than two at three months post-thrombolysis were considered as good outcome. The clinical features between stroke patients with good and with poor outcomes were compared using three different machine learning models (Random Forest, Support Vector Machine and Logistic Regression) to identify which performed best. Two datasets from the other stroke center were included accordingly for external verification and performed with explainable AI models.
ResultsOf the 488 patients enrolled in this study, and 374 (76.6%) patients had favorable outcomes. Patients with higher mRs at 3 months had increased systolic pressure, blood glucose, cholesterol (TC), and 7-day National Institute of Health Stroke Scale (NIHSS) score compared to those with lower mRs. The predictability and the areas under the curves (AUC) for the random forest model was relatively higher than support vector machine and LR models. These findings were further validated in the external dataset and similar results were obtained. The explainable AI model identified the risk factors as well.
ConclusionExplainable AI model is able to identify NIHSS_Day7 is independently efficient in predicting neurological outcomes in patients with ischemic stroke after intravenous thrombolysis.

许可

CC BY 4.0

引用

学术使用请引用 DOI 10.3389/fnins.2023.1146197.s001（source: https://frontiersin.figshare.com/articles/dataset/Data_Sheet_1_Explainable_machine_learning_for_long-term_outcome_prediction_in_two-center_stroke_patients_after_intravenous_thrombolysis_docx/22138913）。