预测房颤患者全因死亡风险的前瞻性数据集（LASSO-Cox模型）

数据集获取（Figshare）

• 源站标题：Data_Sheet_1_Predicting All-Cause Mortality Risk in Atrial Fibrillation Patients: A Novel LASSO-Cox Model Generated From a Prospective Dataset.CSV
• DOI：10.3389/fcvm.2021.730453.s001
• 发布日期：2021-10-18
• 文件数：1 个
• 文件总体积：785.42 KB
• 源站页面：https://frontiersin.figshare.com/articles/dataset/Data_Sheet_1_Predicting_All-Cause_Mortality_Risk_in_Atrial_Fibrillation_Patients_A_Novel_LASSO-Cox_Model_Generated_From_a_Prospective_Dataset_CSV/16824166

命令行下载

curl -L -o "Data_Sheet_1_Predicting All-Cause Mortality Risk in Atrial Fibrillation Patients: A Novel LASSO-Cox Model Generated From a Prospective Dataset.CSV" "https://ndownloader.figshare.com/files/31109914"

文件清单

• Data_Sheet_1_Predicting All-Cause Mortality Risk in Atrial Fibrillation Patients: A Novel LASSO-Cox Model Generated From a Prospective Dataset.CSV（785.42 KB）

文件总体积 785.42 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: Although mortality remains high in patients with atrial fibrillation (AF), there have been limited studies exploring machine learning (ML) models on mortality risk prediction in patients with AF.
Objectives: This study sought to develop an ML model that captures important variables in order to predict all-cause mortality in AF patients.
Methods: In this single center prospective study, an ML-based mortality prediction model was developed and validated using a dataset of 2,012 patients who experienced AF from November 2018 to February 2020 at the First Affiliated Hospital of Shantou University Medical College. The dataset was randomly divided into a training set (70%, n = 1,223) and a validation set (30%, n = 552). A total of 122 features were collected for variable selection. Least absolute shrinkage and selection operator (LASSO) and random forest (RF) algorithms were used for variable selection. Ten ML models were developed using variables selected by LASSO or RF. The best model was selected and compared with conventional risk scores. A nomogram and user-friendly online tool were developed to facilitate the mortality predictions and management recommendations.
Results: Thirteen features were selected by the LASSO regression algorithm. The LASSO-Cox model achieved an area under the curve (AUC) of 0.842 in the training dataset, and 0.854 in the validation dataset. A nomogram based on eight independent features was developed for the prediction of survival at 30, 180, and 365 days following discharge. Both the time dependent receiver operating characteristic (ROC) and decision curve analysis (DCA) showed better performances of the nomogram compared to the CHA2DS2-VASc and HAS-BLED models.
Conclusions: The LASSO-Cox mortality predictive model shows potential benefits in death risk evaluation for AF patients over the 365-day period following discharge. This novel ML approach may also provide physicians with personalized management recommendations.

许可

CC BY 4.0

引用

学术使用请引用 DOI 10.3389/fcvm.2021.730453.s001（source: https://frontiersin.figshare.com/articles/dataset/Data_Sheet_1_Predicting_All-Cause_Mortality_Risk_in_Atrial_Fibrillation_Patients_A_Novel_LASSO-Cox_Model_Generated_From_a_Prospective_Dataset_CSV/16824166）。