KETOS：临床决策支持和机器学习即服务——基于Docker、OMOP-CDM和FHIR Web服务的训练和部署平台

数据集获取（Figshare）

• 源站标题：KETOS: Clinical decision support and machine learning as a service – A training and deployment platform based on Docker, OMOP-CDM, and FHIR Web Services
• DOI：10.1371/journal.pone.0223010
• 发布日期：2019-10-03
• 文件数：5 个
• 文件总体积：4.41 MB
• 源站页面：https://plos.figshare.com/articles/dataset/KETOS_Clinical_decision_support_and_machine_learning_as_a_service_A_training_and_deployment_platform_based_on_Docker_OMOP-CDM_and_FHIR_Web_Services/9936065

命令行下载

curl -L -o "pone.0223010.s001.zip" "https://ndownloader.figshare.com/files/17883866"
curl -L -o "pone.0223010.s002.zip" "https://ndownloader.figshare.com/files/17883869"
curl -L -o "pone.0223010.s003.zip" "https://ndownloader.figshare.com/files/17883872"
curl -L -o "pone.0223010.s004.zip" "https://ndownloader.figshare.com/files/17883875"
curl -L -o "pone.0223010.s005.zip" "https://ndownloader.figshare.com/files/17883878"

文件清单

• pone.0223010.s001.zip（586.84 KB）
• pone.0223010.s002.zip（1.89 MB）
• pone.0223010.s003.zip（1.78 MB）
• pone.0223010.s004.zip（8.91 KB）
• pone.0223010.s005.zip（145.50 KB）

文件总体积 4.41 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

Background and objective
To take full advantage of decision support, machine learning, and patient-level prediction models, it is important that models are not only created, but also deployed in a clinical setting. The KETOS platform demonstrated in this work implements a tool for researchers allowing them to perform statistical analyses and deploy resulting models in a secure environment.
Methods
The proposed system uses Docker virtualization to provide researchers with reproducible data analysis and development environments, accessible via Jupyter Notebook, to perform statistical analysis and develop, train and deploy models based on standardized input data. The platform is built in a modular fashion and interfaces with web services using the Health Level 7 (HL7) Fast Healthcare Interoperability Resources (FHIR) standard to access patient data. In our prototypical implementation we use an OMOP common data model (OMOP-CDM) database. The architecture supports the entire research lifecycle from creating a data analysis environment, retrieving data, and training to final deployment in a hospital setting.
Results
We evaluated the platform by establishing and deploying an analysis and end user application for hemoglobin reference intervals within the University Hospital Erlangen. To demonstrate the potential of the system to deploy arbitrary models, we loaded a colorectal cancer dataset into an OMOP database and built machine learning models to predict patient outcomes and made them available via a web service. We demonstrated both the integration with FHIR as well as an example end user application. Finally, we integrated the platform with the open source DataSHIELD architecture to allow for distributed privacy preserving data analysis and training across networks of hospitals.
Conclusion
The KETOS platform takes a novel approach to data analysis, training and deploying decision support models in a hospital or healthcare setting. It does so in a secure and privacy-preserving manner, combining the flexibility of Docker virtualization with the advantages of standardized vocabularies, a widely applied database schema (OMOP-CDM), and a standardized way to exchange medi

许可

CC BY 4.0

引用

学术使用请引用 DOI 10.1371/journal.pone.0223010（source: https://plos.figshare.com/articles/dataset/KETOS_Clinical_decision_support_and_machine_learning_as_a_service_A_training_and_deployment_platform_based_on_Docker_OMOP-CDM_and_FHIR_Web_Services/9936065）。