数据表1_接受心脏再同步治疗患者死亡预测因素的性别特异性模式：机器学习方法

数据集获取（Figshare）

• 源站标题：Data_Sheet_1_Sex-Specific Patterns of Mortality Predictors Among Patients Undergoing Cardiac Resynchronization Therapy: A Machine Learning Approach.docx
• DOI：10.3389/fcvm.2021.611055.s001
• 发布日期：2021-02-25
• 文件数：1 个
• 文件总体积：645.16 KB
• 源站页面：https://frontiersin.figshare.com/articles/dataset/Data_Sheet_1_Sex-Specific_Patterns_of_Mortality_Predictors_Among_Patients_Undergoing_Cardiac_Resynchronization_Therapy_A_Machine_Learning_Approach_docx/14112239

命令行下载

curl -L -o "Data_Sheet_1_Sex-Specific Patterns of Mortality Predictors Among Patients Undergoing Cardiac Resynchronization Therapy: A Machine Learning Approach.docx" "https://ndownloader.figshare.com/files/26613947"

文件清单

• Data_Sheet_1_Sex-Specific Patterns of Mortality Predictors Among Patients Undergoing Cardiac Resynchronization Therapy: A Machine Learning Approach.docx（645.16 KB）

文件总体积 645.16 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: The relative importance of variables explaining sex-related differences in outcomes is scarcely explored in patients undergoing cardiac resynchronization therapy (CRT). We sought to implement and evaluate machine learning (ML) algorithms for the prediction of 1- and 3-year all-cause mortality in CRT patients. We also aimed to assess the sex-specific differences in predictors of mortality utilizing ML.
Methods: Using a retrospective registry of 2,191 CRT patients, ML models were implemented in 6 partially overlapping patient subsets (all patients, females, or males with 1- or 3-year follow-up). Each cohort was randomly split into training (80%) and test sets (20%). After hyperparameter tuning in the training sets, the best performing algorithm was evaluated in the test sets. Model discrimination was quantified using the area under the receiver-operating characteristic curves (AUC). The most important predictors were identified using the permutation feature importances method.
Results: Conditional inference random forest exhibited the best performance with AUCs of 0.728 (0.645–0.802) and 0.732 (0.681–0.784) for the prediction of 1- and 3-year mortality, respectively. Etiology of heart failure, NYHA class, left ventricular ejection fraction, and QRS morphology had higher predictive power, whereas hemoglobin was less important in females compared to males. The importance of atrial fibrillation and age increased, while the importance of serum creatinine decreased from 1- to 3-year follow-up in both sexes.
Conclusions: Using ML techniques in combination with easily obtainable clinical features, our models effectively predicted 1- and 3-year all-cause mortality in CRT patients. Sex-specific patterns of predictors were identified, showing a dynamic variation over time.

许可

CC BY 4.0

引用

学术使用请引用 DOI 10.3389/fcvm.2021.611055.s001（source: https://frontiersin.figshare.com/articles/dataset/Data_Sheet_1_Sex-Specific_Patterns_of_Mortality_Predictors_Among_Patients_Undergoing_Cardiac_Resynchronization_Therapy_A_Machine_Learning_Approach_docx/14112239）。