使用机器学习预测急性呼吸衰竭患者心房颤动的数据集（MIMIC-III和MIMIC-IV）

数据集获取（Figshare）

• 源站标题：Data Sheet 1_Predicting atrial fibrillation in patients with acute respiratory failure using machine learning: application of the MIMIC-III and MIMIC-IV datasets.csv
• DOI：10.3389/fcvm.2025.1696609.s001
• 发布日期：2025-10-09
• 文件数：1 个
• 文件总体积：2.07 MB
• 源站页面：https://frontiersin.figshare.com/articles/dataset/Data_Sheet_1_Predicting_atrial_fibrillation_in_patients_with_acute_respiratory_failure_using_machine_learning_application_of_the_MIMIC-III_and_MIMIC-IV_datasets_csv/30315367

命令行下载

curl -L -o "Data Sheet 1_Predicting atrial fibrillation in patients with acute respiratory failure using machine learning: application of the MIMIC-III and MIMIC-.csv" "https://ndownloader.figshare.com/files/58586851"

文件清单

• Data Sheet 1_Predicting atrial fibrillation in patients with acute respiratory failure using machine learning: application of the MIMIC-III and MIMIC-.csv（2.07 MB）

文件总体积 2.07 MB（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

BackgroundAcute respiratory failure (ARF) and atrial fibrillation (AF) are common diseases. This study established a predictive model for the risk of atrial fibrillation in patients with ARF, aiming to provide tools for clinical application.
MethodsThis study examined the data of 21,594 patients in the MIMIC-IV database, including factors such as age, vital signs, and laboratory results on the first day of admission. Six feature selection techniques and six machine learning algorithms were used to construct the prediction model, and then the prediction model was verified using the MIMIC-III database. Evaluate the performance of the model through the comparison of results.
ResultsA total of 59 predictor variables were identified, among which age was the most important factor. These variables are used to establish predictive models. The verification results show that the XGBoost model (AUC: 0.816) and the Random Forest (RF) model (AUC: 0.822) have the best performance. This study presents the first predictive model for atrial fibrillation in patients with acute respiratory failure.
ConclusionsBoth the XGBoost and RF models demonstrated outstanding performance. These findings will make significant contributions to the diagnosis of clinical complications and the resolution of public health issues.

许可

CC BY 4.0

引用

学术使用请引用 DOI 10.3389/fcvm.2025.1696609.s001（source: https://frontiersin.figshare.com/articles/dataset/Data_Sheet_1_Predicting_atrial_fibrillation_in_patients_with_acute_respiratory_failure_using_machine_learning_application_of_the_MIMIC-III_and_MIMIC-IV_datasets_csv/30315367）。