基于12导联心电图的人工智能预测急诊患者紧急血运重建需求

数据集获取（Figshare）

• 源站标题：Artificial intelligence to predict needs for urgent revascularization from 12-leads electrocardiography in emergency patients
• DOI：10.1371/journal.pone.0210103
• 发布日期：2019-01-09
• 文件数：7 个
• 文件总体积：701.74 KB
• 源站页面：https://plos.figshare.com/articles/dataset/Artificial_intelligence_to_predict_needs_for_urgent_revascularization_from_12-leads_electrocardiography_in_emergency_patients/7568150

命令行下载

curl -L -o "pone.0210103.s001.docx" "https://ndownloader.figshare.com/files/14061698"
curl -L -o "pone.0210103.s002.py" "https://ndownloader.figshare.com/files/14061701"
curl -L -o "pone.0210103.s003.hdf5" "https://ndownloader.figshare.com/files/14061704"
curl -L -o "pone.0210103.s004.xlsx" "https://ndownloader.figshare.com/files/14061707"
curl -L -o "pone.0210103.s005.xlsx" "https://ndownloader.figshare.com/files/14061710"
curl -L -o "pone.0210103.s006.xlsx" "https://ndownloader.figshare.com/files/14061713"
curl -L -o "pone.0210103.s007.xlsx" "https://ndownloader.figshare.com/files/14061716"

文件清单

• pone.0210103.s001.docx（35.16 KB）
• pone.0210103.s002.py（2.19 KB）
• pone.0210103.s003.hdf5（607.83 KB）
• pone.0210103.s004.xlsx（13.48 KB）
• pone.0210103.s005.xlsx（13.17 KB）
• pone.0210103.s006.xlsx（14.95 KB）
• pone.0210103.s007.xlsx（14.97 KB）

文件总体积 701.74 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
Patient with acute coronary syndrome benefits from early revascularization. However, methods for the selection of patients who require urgent revascularization from a variety of patients visiting the emergency room with chest symptoms is not fully established. Electrocardiogram is an easy and rapid procedure, but may contain crucial information not recognized even by well-trained physicians.
Objective
To make a prediction model for the needs for urgent revascularization from 12-lead electrocardiogram recorded in the emergency room.
Method
We developed an artificial intelligence model enabling the detection of hidden information from a 12-lead electrocardiogram recorded in the emergency room. Electrocardiograms obtained from consecutive patients visiting the emergency room at Keio University Hospital from January 2012 to April 2018 with chest discomfort was collected. These data were splitted into validation and derivation dataset with no duplication in each dataset. The artificial intelligence model was constructed to select patients who require urgent revascularization within 48 hours. The model was trained with the derivation dataset and tested using the validation dataset.
Results
Of the consecutive 39,619 patients visiting the emergency room with chest discomfort, 362 underwent urgent revascularization. Of them, 249 were included in the derivation dataset and the remaining 113 were included in validation dataset. For the control, 300 were randomly selected as derivation dataset and another 130 patients were randomly selected for validation dataset from the 39,317 who did not undergo urgent revascularization. On validation, our artificial intelligence model had predictive value of the c-statistics 0.88 (95% CI 0.84–0.93) for detecting patients who required urgent revascularization.
Conclusions
Our artificial intelligence model provides information to select patients who need urgent revascularization from only 12-leads electrocardiogram in those visiting the emergency room with chest discomfort.

许可

CC BY 4.0

引用

学术使用请引用 DOI 10.1371/journal.pone.0210103（source: https://plos.figshare.com/articles/dataset/Artificial_intelligence_to_predict_needs_for_urgent_revascularization_from_12-leads_electrocardiography_in_emergency_patients/7568150）。