迟发性脑缺血预测数据表2

数据集获取（Figshare）

• 源站标题：Data_Sheet_2_Comparison of Conventional Logistic Regression and Machine Learning Methods for Predicting Delayed Cerebral Ischemia After Aneurysmal Subarachnoid Hemorrhage: A Multicentric Observational Cohort Study.CSV
• DOI：10.3389/fnagi.2022.857521.s002
• 发布日期：2022-06-17
• 文件数：1 个
• 文件总体积：8.94 KB
• 源站页面：https://frontiersin.figshare.com/articles/dataset/Data_Sheet_2_Comparison_of_Conventional_Logistic_Regression_and_Machine_Learning_Methods_for_Predicting_Delayed_Cerebral_Ischemia_After_Aneurysmal_Subarachnoid_Hemorrhage_A_Multicentric_Observational_Cohort_Study_CSV/20086739

命令行下载

curl -L -o "Data_Sheet_2_Comparison of Conventional Logistic Regression and Machine Learning Methods for Predicting Delayed Cerebral Ischemia After Aneurysmal Sub.CSV" "https://ndownloader.figshare.com/files/35929184"

文件清单

• Data_Sheet_2_Comparison of Conventional Logistic Regression and Machine Learning Methods for Predicting Delayed Cerebral Ischemia After Aneurysmal Sub.CSV（8.94 KB）

文件总体积 8.94 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

BackgroundTimely and accurate prediction of delayed cerebral ischemia is critical for improving the prognosis of patients with aneurysmal subarachnoid hemorrhage. Machine learning (ML) algorithms are increasingly regarded as having a higher prediction power than conventional logistic regression (LR). This study aims to construct LR and ML models and compare their prediction power on delayed cerebral ischemia (DCI) after aneurysmal subarachnoid hemorrhage (aSAH).
MethodsThis was a multicenter, retrospective, observational cohort study that enrolled patients with aneurysmal subarachnoid hemorrhage from five hospitals in China. A total of 404 aSAH patients were prospectively enrolled. We randomly divided the patients into training (N = 303) and validation cohorts (N = 101) according to a ratio of 75–25%. One LR and six popular ML algorithms were used to construct models. The area under the receiver operating characteristic curve (AUC), accuracy, balanced accuracy, confusion matrix, sensitivity, specificity, calibration curve, and Hosmer–Lemeshow test were used to assess and compare the model performance. Finally, we calculated each feature of importance.
ResultsA total of 112 (27.7%) patients developed DCI. Our results showed that conventional LR with an AUC value of 0.824 (95%CI: 0.73–0.91) in the validation cohort outperformed k-nearest neighbor, decision tree, support vector machine, and extreme gradient boosting model with the AUCs of 0.792 (95%CI: 0.68–0.9, P = 0.46), 0.675 (95%CI: 0.56–0.79, P In this multicenter study, we found that several ML methods, particularly RF, outperformed conventional LR. Furthermore, an online prediction tool based on the RF model was developed to identify patients at high risk for DCI after SAH and facilitate timely interventions.
Clinical Trial Registrationhttp://www.chictr.org.cn, Unique identifier: ChiCTR2100044448.

许可

CC BY 4.0

引用

学术使用请引用 DOI 10.3389/fnagi.2022.857521.s002（source: https://frontiersin.figshare.com/articles/dataset/Data_Sheet_2_Comparison_of_Conventional_Logistic_Regression_and_Machine_Learning_Methods_for_Predicting_Delayed_Cerebral_Ischemia_After_Aneurysmal_Subarachnoid_Hemorrhage_A_Multicentric_Observational_Cohort_Study_CSV/20086739）。