论文标题
FHIR上的无服务器：在云上部署用于医疗保健的机器学习模型
Serverless on FHIR: Deploying machine learning models for healthcare on the cloud
论文作者
论文摘要
机器学习（ML）在实施数字健康方面起着至关重要的作用。硬件和软件工具的民主化的进步彻底改变了机器学习。但是，ML模型的部署 - 要执行的任务的数学表示形式 - 在护理点上有效而有效的临床决策支持仍然是一个挑战。 ML模型以高的周转率不断提高其准确性和预测能力。下游健康信息系统消耗的更新模型对于患者安全至关重要。我们引入了功能分类法和四层架构，用于基于云的数字健康模型部署。这四个层是可维护性的容器化微服务，无服务器的架构可扩展性，作为可移植性的服务以及可发现性的FHIR模式。我们将此架构称为FHIR上的无服务器，并将其作为部署数字健康应用程序的标准，可以通过EMR和可视化工具等下游系统消费。
Machine Learning (ML) plays a vital role in implementing digital health. The advances in hardware and the democratization of software tools have revolutionized machine learning. However, the deployment of ML models -- the mathematical representation of the task to be performed -- for effective and efficient clinical decision support at the point of care is still a challenge. ML models undergo constant improvement of their accuracy and predictive power with a high turnover rate. Updating models consumed by downstream health information systems is essential for patient safety. We introduce a functional taxonomy and a four-tier architecture for cloud-based model deployment for digital health. The four tiers are containerized microservices for maintainability, serverless architecture for scalability, function as a service for portability and FHIR schema for discoverability. We call this architecture Serverless on FHIR and propose this as a standard to deploy digital health applications that can be consumed by downstream systems such as EMRs and visualization tools.
