数据集获取

git clone https://github.com/xup6fup/AIDE.git

curl -L -o repo.zip https://github.com/xup6fup/AIDE/archive/refs/heads/main.zip
unzip repo.zip

数据集大小约 0.05 GB。

源站 README 摘录（使用方式）

Artificial Intelligence enabled Dyskalemia using Electrocardiogram (AIDE) trial

The Artificial Intelligence enabled Dyskalemia using Electrocardiogram (AIDE) trial (registered at clinicaltrial.gov: NCT05118022) is a pioneering clinical study designed to improve the early detection and treatment of dangerous potassium imbalances in patients. Potassium is a vital electrolyte for heart function, and when its levels become too high (hyperkalemia) or too low (hypokalemia), it can lead to severe health risks, including heart rhythm disturbances and even cardiac arrest. Traditionally, detecting these imbalances relies on blood tests, which can delay treatment, or on manual interpretation of ECGs—a method that may miss subtle signs of dyskalemia.
To address these challenges, the AIDE trial integrated an AI-powered system into the electronic health records of emergency departments. This system analyzes patients’ ECGs in real time and automatically alerts physicians when it detects patterns suggesting a significant potassium imbalance. By providing an immediate “pop-up” notification, the AI tool is intended to prompt faster clinical decisions and timely intervention, particularly for patients with hyperkalemia, where rapid treatment is critical.
In this randomized controlled trial, physicians were divided into two groups: one received the AI alert during patient care, while the other did not. The trial’s primary goal was to determine whether the AI alert could lead to more prompt and effective treatment for patients with abnormal ECG findings associated with dyskalemia. Early results indicated that doctors who received the alert were more likely to initiate treatment quickly, potentially reducing the risk of serious complications.
Ultimately, the AIDE trial aims to demonstrate that incorporating AI into everyday clinical practice can enhance patient safety by supporting healthcare professionals with accurate, real-time diagnostic information. This innovative approach marks an important step toward harnessing advanced technology to improve outcomes in emergency medicine.
This repository is carried out using the software environment R version 3.4.4. The overall file structure is as follows:

AIDE
├── code
│   ├── ...
├── data
│   ├── ...
├── result
│   ├── ...

The relevant syntax for data analysis

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。

完整仓库：github.com/xup6fup/AIDE