扩张型心肌病患者缺血性卒中风险因素及贝叶斯网络预测模型表1

数据集获取（Figshare）

• 源站标题：Table_1_Risk factors and a Bayesian network model to predict ischemic stroke in patients with dilated cardiomyopathy.docx
• DOI：10.3389/fnins.2022.1043922.s001
• 发布日期：2022-11-09
• 文件数：1 个
• 文件总体积：14.01 KB
• 源站页面：https://frontiersin.figshare.com/articles/dataset/Table_1_Risk_factors_and_a_Bayesian_network_model_to_predict_ischemic_stroke_in_patients_with_dilated_cardiomyopathy_docx/21522315

命令行下载

curl -L -o "Table_1_Risk factors and a Bayesian network model to predict ischemic stroke in patients with dilated cardiomyopathy.docx" "https://ndownloader.figshare.com/files/38151840"

文件清单

• Table_1_Risk factors and a Bayesian network model to predict ischemic stroke in patients with dilated cardiomyopathy.docx（14.01 KB）

文件总体积 14.01 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

ObjectiveThis study aimed to identify risk factors and create a predictive model for ischemic stroke (IS) in patients with dilated cardiomyopathy (DCM) using the Bayesian network (BN) approach.
Materials and methodsWe collected clinical data of 634 patients with DCM treated at three referral management centers in Beijing between 2016 and 2021, including 127 with and 507 without IS. The patients were randomly divided into training (441 cases) and test (193 cases) sets at a ratio of 7:3. A BN model was established using the Tabu search algorithm with the training set data and verified with the test set data. The BN and logistic regression models were compared using the area under the receiver operating characteristic curve (AUC).
ResultsMultivariate logistic regression analysis showed that hypertension, hyperlipidemia, atrial fibrillation/flutter, estimated glomerular filtration rate (eGFR), and intracardiac thrombosis were associated with IS. The BN model found that hyperlipidemia, atrial fibrillation (AF) or atrial flutter, eGFR, and intracardiac thrombosis were closely associated with IS. Compared to the logistic regression model, the BN model for IS performed better or equally well in the training and test sets, with respective accuracies of 83.7 and 85.5%, AUC of 0.763 [95% confidence interval (CI), 0.708–0.818] and 0.822 (95% CI, 0.748–0.896), sensitivities of 20.2 and 44.2%, and specificities of 98.3 and 97.3%.
ConclusionHypertension, hyperlipidemia, AF or atrial flutter, low eGFR, and intracardiac thrombosis were good predictors of IS in patients with DCM. The BN model was superior to the traditional logistic regression model in predicting IS in patients with DCM and is, therefore, more suitable for early IS detection and diagnosis, and could help prevent the occurrence and recurrence of IS in this patient cohort.

许可

CC BY 4.0

引用

学术使用请引用 DOI 10.3389/fnins.2022.1043922.s001（source: https://frontiersin.figshare.com/articles/dataset/Table_1_Risk_factors_and_a_Bayesian_network_model_to_predict_ischemic_stroke_in_patients_with_dilated_cardiomyopathy_docx/21522315）。