SepsisAI Orchestrator：一个容器化和可扩展的平台，用于部署AI模型和实时监控以实现早期败血症检测
SepsisAI Orchestrator: A Containerized and Scalable Platform for Deploying AI Models and Real-Time Monitoring in Early Sepsis Detection
AI总结：
本文提出SepsisAI-Orchestrator平台，通过整合HL7 FHIR启发的临床文档架构（CDA）预处理、NoSQL存储、容器化LightGBM分类器和Streamlit临床仪表板，解决了早期败血症检测中AI模型部署的挑战，并通过负载测试展示了U型扩展行为。
摘要：
尽管在临床机器学习文献中预测结果强劲，但将这些模型转化为床边使用仍然受限于系统层面的障碍：异构数据表示、缺乏标准化的部署流程以及研究原型与医院环境的并发性和延迟需求之间的不匹配。我们提出了SepsisAI-Orchestrator，一个开源的模块化平台，旨在解决早期败血症检测中的部署缺口。该平台集成了HL7 FHIR启发的临床文档架构（CDA）预处理、NoSQL存储、通过REST API服务的容器化LightGBM分类器和Streamlit临床仪表板，并通过Docker和Kubernetes进行协调。一个之前已验证的LightGBM模型（在PhysioNet 2019上的F1值为0.87-0.94）在不进行修改的情况下被重用；贡献在于周围基础设施及其在负载下的实证表征。使用k6进行50-1000个并发虚拟用户测试，我们发现副本数量必须与主机的物理CPU线程数匹配：在12线程CPU上从3个副本扩展到12个副本，将p95延迟从3.3秒减少到1.41秒（减少57.3%）并消除所有请求失败，而过度配置到24或48个副本则由于调度器竞争导致性能下降。据我们所知，这种U型扩展行为此前尚未对临床AI推理工作负载进行量化。我们不声称具有前瞻性临床验证。源代码和部署清单可在https://github.com/nucleusai/sepsisai-orchestrator获取。
英文摘要：
Despite strong predictive results in the clinical machine learning literature, the translation of these models into bedside use remains limited by systems-level barriers: heterogeneous data representations, the absence of standardized deployment workflows, and a mismatch between research prototypes and the concurrency and latency requirements of hospital environments. We present the SepsisAI-Orchestrator, an open-source modular platform that addresses this deployment gap for early sepsis detection. The platform integrates HL7 FHIR-inspired Clinical Document Architecture (CDA) preprocessing, NoSQL storage, a containerized LightGBM classifier served via REST APIs, and a Streamlit clinical dashboard, orchestrated with Docker and Kubernetes. A previously validated LightGBM model (F1 0.87-0.94 on PhysioNet 2019) is reused without modification; the contribution lies in the surrounding infrastructure and its empirical characterization under load. Using k6 with 50-1000 concurrent virtual users, we find that replica count must be matched to the physical CPU thread count of the host: scaling from 3 to 12 replicas on a 12-thread CPU reduces p95 latency from 3.3s to 1.41s (57.3% reduction) and eliminates all request failures, while over-provisioning to 24 or 48 replicas degrades performance due to scheduler contention. To our knowledge this U-shaped scaling behavior has not been quantified previously for clinical AI inference workloads. We do not claim prospective clinical validation. Source code and deployment manifests are available at https://github.com/nucleusai/sepsisai-orchestrator.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
