Table 1_预测具有脑出血高风险的缺血性脑卒中认知障碍患者的认知下降：一种机器学习方法

数据集获取（Figshare）

• 源站标题：Table 1_Predicting cognitive decline in cognitively impaired patients with ischemic stroke with high risk of cerebral hemorrhage: a machine learning approach.docx
• DOI：10.3389/fneur.2025.1569073.s002
• 发布日期：2025-07-25
• 文件数：1 个
• 文件总体积：20.87 KB
• 源站页面：https://frontiersin.figshare.com/articles/dataset/Table_1_Predicting_cognitive_decline_in_cognitively_impaired_patients_with_ischemic_stroke_with_high_risk_of_cerebral_hemorrhage_a_machine_learning_approach_docx/29647577

命令行下载

curl -L -o "Table 1_Predicting cognitive decline in cognitively impaired patients with ischemic stroke with high risk of cerebral hemorrhage: a machine learning a.docx" "https://ndownloader.figshare.com/files/56558867"

文件清单

• Table 1_Predicting cognitive decline in cognitively impaired patients with ischemic stroke with high risk of cerebral hemorrhage: a machine learning a.docx（20.87 KB）

文件总体积 20.87 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 objectiveCognitive decline progresses rapidly in stroke patients, increasing risks of stroke recurrence. Predicting deterioration within a year in patients with poststroke cognitive impairment (PSCI) could guide targeted interventions for dementia prevention and better prognosis. In this PreventIon of CArdiovascular events in iSchemic Stroke patients with high risk of cerebral hemOrrhage for reducing cognitive decline substudy, machine learning on clinical and imaging data was used to predict cognitive decline over 9 months in PSCI patients.
MethodsThis retrospective study included 109 patients with acute ischemic stroke and high-risk cerebral hemorrhage with PSCI (baseline Korean-Mini Mental Status Examination [K-MMSE] CatBoost outperformed the other models in classifying cognitive decliners within 9 months. In the test set, CatBoost achieved a mean area under the curve (AUC) of 0.897, with an accuracy of 0.873; other models performed as follows: logistic regression (AUC 0.775), AdaBoost (AUC 0.767), and XGBoost (AUC 0.722). Higher baseline K-MMSE scores (total, language, orientation to place, and recall), longer interval between stroke and baseline K-MMSE, initial National Institutes of Health Stroke Scale scores, and lesion volume ratio were identified as key predictors of cognitive decline in CatBoost. Cognitive decliners showed longer interval between stroke onset and pharmacotherapy initiation than non-decliners.
ConclusionCatBoost effectively recognized patients with ischemic stroke at high risk of cognitive decline over 9 months. Recognizing these high-risk individuals and their risk and protective factors allows for timely and targeted interventions to improve prognosis in PSCI patients.

许可

CC BY 4.0

引用

学术使用请引用 DOI 10.3389/fneur.2025.1569073.s002（source: https://frontiersin.figshare.com/articles/dataset/Table_1_Predicting_cognitive_decline_in_cognitively_impaired_patients_with_ischemic_stroke_with_high_risk_of_cerebral_hemorrhage_a_machine_learning_approach_docx/29647577）。