Table_1_跨医院数据利用的本地与分布式机器学习：TAVI结局预测应用

数据集获取（Figshare）

• 源站标题：Table_1_Local and Distributed Machine Learning for Inter-hospital Data Utilization: An Application for TAVI Outcome Prediction.docx
• DOI：10.3389/fcvm.2021.787246.s001
• 发布日期：2021-11-12
• 文件数：1 个
• 文件总体积：23.49 KB
• 源站页面：https://frontiersin.figshare.com/articles/dataset/Table_1_Local_and_Distributed_Machine_Learning_for_Inter-hospital_Data_Utilization_An_Application_for_TAVI_Outcome_Prediction_docx/16993888

命令行下载

curl -L -o "Table_1_Local and Distributed Machine Learning for Inter-hospital Data Utilization: An Application for TAVI Outcome Prediction.docx" "https://ndownloader.figshare.com/files/31435156"

文件清单

• Table_1_Local and Distributed Machine Learning for Inter-hospital Data Utilization: An Application for TAVI Outcome Prediction.docx（23.49 KB）

文件总体积 23.49 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: Machine learning models have been developed for numerous medical prognostic purposes. These models are commonly developed using data from single centers or regional registries. Including data from multiple centers improves robustness and accuracy of prognostic models. However, data sharing between multiple centers is complex, mainly because of regulations and patient privacy issues.
Objective: We aim to overcome data sharing impediments by using distributed ML and local learning followed by model integration. We applied these techniques to develop 1-year TAVI mortality estimation models with data from two centers without sharing any data.
Methods: A distributed ML technique and local learning followed by model integration was used to develop models to predict 1-year mortality after TAVI. We included two populations with 1,160 (Center A) and 631 (Center B) patients. Five traditional ML algorithms were implemented. The results were compared to models created individually on each center.
Results: The combined learning techniques outperformed the mono-center models. For center A, the combined local XGBoost achieved an AUC of 0.67 (compared to a mono-center AUC of 0.65) and, for center B, a distributed neural network achieved an AUC of 0.68 (compared to a mono-center AUC of 0.64).
Conclusion: This study shows that distributed ML and combined local models techniques, can overcome data sharing limitations and result in more accurate models for TAVI mortality estimation. We have shown improved prognostic accuracy for both centers and can also be used as an alternative to overcome the problem of limited amounts of data when creating prognostic models.

许可

CC BY 4.0

引用

学术使用请引用 DOI 10.3389/fcvm.2021.787246.s001（source: https://frontiersin.figshare.com/articles/dataset/Table_1_Local_and_Distributed_Machine_Learning_for_Inter-hospital_Data_Utilization_An_Application_for_TAVI_Outcome_Prediction_docx/16993888）。