前交通动脉瘤破裂后出院临床结果预测数据表1

数据集获取（Figshare）

• 源站标题：Data_Sheet_1_Prediction of Clinical Outcome at Discharge After Rupture of Anterior Communicating Artery Aneurysm Using the Random Forest Technique.docx
• DOI：10.3389/fneur.2020.538052.s001
• 发布日期：2020-10-29
• 文件数：1 个
• 文件总体积：1.38 MB
• 源站页面：https://frontiersin.figshare.com/articles/dataset/Data_Sheet_1_Prediction_of_Clinical_Outcome_at_Discharge_After_Rupture_of_Anterior_Communicating_Artery_Aneurysm_Using_the_Random_Forest_Technique_docx/13159247

命令行下载

curl -L -o "Data_Sheet_1_Prediction of Clinical Outcome at Discharge After Rupture of Anterior Communicating Artery Aneurysm Using the Random Forest Technique.docx" "https://ndownloader.figshare.com/files/25307015"

文件清单

• Data_Sheet_1_Prediction of Clinical Outcome at Discharge After Rupture of Anterior Communicating Artery Aneurysm Using the Random Forest Technique.docx（1.38 MB）

文件总体积 1.38 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

Background: Aneurysmal subarachnoid hemorrhage (SAH) is a devastating disease. Anterior communicating artery (ACoA) aneurysm is the most frequent location of intracranial aneurysms. The purpose of this study is to predict the clinical outcome at discharge after rupture of ACoA aneurysms using the random forest machine learning technique.
Methods: A total of 607 patients with ruptured ACoA aneurysms were included in this study between December 2007 and January 2016. In addition to basic clinical variables, 12 aneurysm morphologic parameters were evaluated. A multivariate logistic regression analysis was performed to determine the independent predictors of poor outcome. Of the 607 patients, 485 patients were randomly selected for training and the remaining for internal testing. The random forest model was developed using the training data set. An additional 202 patients from February 2016 to December 2017 were collected for externally validating the model. The prediction performance of the random forest model was compared with two radiologists.
Results: Patients’’ age (odds ratio [OR] = 1.04), ventilated breathing status (OR = 4.23), World Federation of Neurosurgical Societies (WFNS) grade (OR = 2.13), and Fisher grade (OR = 1.50) are significantly associated with poor outcome. None of the investigated morphological parameters of ACoA aneurysm is an independent predictor of poor outcome. The developed random forest model achieves sensitivities of 78.3% for internal test and 73.8% for external test. The areas under receiver operating characteristic (ROC) curve of the random forest model were 0.90 for the internal test and 0.84 for the external test. Both sensitivities and areas under ROC curves of our model are superior to those of two raters in both internal and external tests.
Conclusions: The random forest model presents good performance in predicting the outcome after rupture of ACoA aneurysms, which may aid in clinical decision making.

许可

CC BY 4.0

引用

学术使用请引用 DOI 10.3389/fneur.2020.538052.s001（source: https://frontiersin.figshare.com/articles/dataset/Data_Sheet_1_Prediction_of_Clinical_Outcome_at_Discharge_After_Rupture_of_Anterior_Communicating_Artery_Aneurysm_Using_the_Random_Forest_Technique_docx/13159247）。