面向实时临床工作负载的事件驱动云原生可穿戴设备分析框架
An Event-Driven Cloud-Native Wearable Analytics Framework for Real-Time Clinical Workloads
AI总结：
本文提出一种事件驱动云原生可穿戴分析框架，采用微服务、FHIR标准及奖章湖仓架构，实现大规模实时生命体征监测，性能达标且合规，为医疗场景提供支撑。
AI中文摘要：
利用消费级可穿戴设备进行连续生理监测为临床护理与研究提供了变革性机遇，但设备异质性、专有数据格式及严格的监管要求阻碍了其整合。本文提出一种事件驱动的云原生系统，旨在大规模摄取、标准化并分析来自可穿戴设备的高频生命体征数据，且不受厂商锁定限制。该系统设计采用多层微服务架构，借助集群编排实现；数据采集通过跨平台移动应用完成，该应用利用原生健康框架，确保在碎片化设备生态系统中的兼容性。为解决互操作性问题，本文实现了基于流处理引擎及专用服务的事件驱动转换管道，将原始测量数据映射至用于医疗互操作性的FHIR标准。本文提出的新型依赖感知FHIR最小化方案可在保持资源无损重构的同时降低存储开销。此外，该平台集成了基于奖章湖仓架构的模块化数据分析与机器学习层，支持从实时流处理到模型服务的完整机器学习生命周期。性能评估表明，该摄取管道每秒可维持50次完整摄取请求，中位响应时间低于8毫秒，满足实时患者监测的低延迟要求。本文的开源实现通过基于角色的访问控制与安全的服务间通信符合监管合规标准，为在机构医疗环境中部署基于可穿戴设备的监测系统以支持临床决策与研究工作负载提供了坚实基础。
英文摘要：
Continuous physiological monitoring using consumer-grade wearables offers a transformative opportunity for clinical care and research, yet integration remains hindered by device heterogeneity, proprietary data formats, and strict regulatory requirements. We present an event-driven, cloud-native system designed to ingest, normalize, and analyze high-frequency vital signs from wearables at scale and without vendor lock-in. The system design proposes a multi-layered microservice architecture using cluster orchestration. Data acquisition is handled via a cross-platform mobile application that leverages native health frameworks, ensuring compatibility across fragmented device ecosystems. To address interoperability, we implement an event-driven transformation pipeline using stream processing engines and specialized services to map raw measurements to the FHIR standard for medical interoperability. Our novel dependency-aware FHIR minimization scheme reduces storage overhead while maintaining lossless resource reconstruction. Furthermore, the platform integrates a modular data analytics and machine learning layer based on a medallion lakehouse architecture, supporting the full machine learning lifecycle from real-time stream processing to model serving. Performance evaluation demonstrates that the ingestion pipeline sustains 50 full ingestion requests per second with median response times under 8 ms, satisfying the low-latency requirements for real-time patient monitoring. Our open-source implementation adheres to regulatory compliance standards through role-based access control and secure service-to-service communication, providing a robust foundation for deploying wearable-based monitoring in institutional healthcare settings for clinical decision support and research workloads.
