预测急性缺血性卒中6个月不良结局的机器学习数据表

数据集获取（Figshare）

• 源站标题：Data_Sheet_1_Predicting 6-Month Unfavorable Outcome of Acute Ischemic Stroke Using Machine Learning.docx
• DOI：10.3389/fneur.2020.539509.s001
• 发布日期：2020-11-19
• 文件数：1 个
• 文件总体积：33.76 KB
• 源站页面：https://frontiersin.figshare.com/articles/dataset/Data_Sheet_1_Predicting_6-Month_Unfavorable_Outcome_of_Acute_Ischemic_Stroke_Using_Machine_Learning_docx/13258079

命令行下载

curl -L -o "Data_Sheet_1_Predicting 6-Month Unfavorable Outcome of Acute Ischemic Stroke Using Machine Learning.docx" "https://ndownloader.figshare.com/files/25528415"

文件清单

• Data_Sheet_1_Predicting 6-Month Unfavorable Outcome of Acute Ischemic Stroke Using Machine Learning.docx（33.76 KB）

文件总体积 33.76 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 Purpose: Accurate prediction of functional outcome after stroke would provide evidence for reasonable post-stroke management. This study aimed to develop a machine learning-based prediction model for 6-month unfavorable functional outcome in Chinese acute ischemic stroke (AIS) patient.
Methods: We collected AIS patients at National Advanced Stroke Center of Nanjing First Hospital (China) between September 2016 and March 2019. The unfavorable outcome was defined as modified Rankin Scale score (mRS) 3–6 at 6-month. We developed five machine-learning models (logistic regression, support vector machine, random forest classifier, extreme gradient boosting, and fully-connected deep neural network) and assessed the discriminative performance by the area under the receiver-operating characteristic curve. We also compared them to the Houston Intra-arterial Recanalization Therapy (HIAT) score, the Totaled Health Risks in Vascular Events (THRIVE) score, and the NADE nomogram.
Results: A total of 1,735 patients were included into this study, and 541 (31.2%) of them had unfavorable outcomes. Incorporating age, National Institutes of Health Stroke Scale score at admission, premorbid mRS, fasting blood glucose, and creatinine, there were similar predictive performance between our machine-learning models, while they are significantly better than HIAT score, THRIVE score, and NADE nomogram.
Conclusions: Compared with the HIAT score, the THRIVE score, and the NADE nomogram, the RFC model can improve the prediction of 6-month outcome in Chinese AIS patients.

许可

CC BY 4.0

引用

学术使用请引用 DOI 10.3389/fneur.2020.539509.s001（source: https://frontiersin.figshare.com/articles/dataset/Data_Sheet_1_Predicting_6-Month_Unfavorable_Outcome_of_Acute_Ischemic_Stroke_Using_Machine_Learning_docx/13258079）。