FHIR上的无服务器：在云上部署医疗保健的机器学习模型
Serverless on FHIR: Deploying machine learning models for healthcare on the cloud
摘要：
机器学习（ML）在实现数字健康方面发挥着至关重要的作用。硬件的进步和软件工具的民主化已经彻底改变了机器学习。然而，部署ML模型（待执行任务的数学表示）以在护理点提供有效和高效的临床决策支持仍然是一个挑战。ML模型的准确性和预测能力不断提高，周转率很高。更新下游健康信息系统使用的模型对于患者安全至关重要。我们引入了一个功能分类和四层架构，用于基于云的数字健康模型部署。这四层是用于可维护性的容器化微服务，用于可扩展性的无服务器架构，用于可移植性的功能作为服务以及用于可扩展性的FHIR模式。我们在FHIR上将这种架构称为无服务器，并将其作为部署数字健康应用程序的标准，这些应用程序可以由EMR和可视化工具等下游系统使用。
英文摘要：
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.
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