The advent of cost effective cloud computing over the past decade and ever-growing accumulation of high-fidelity clinical data in a modern hospital setting is leading to new opportunities for translational medicine. Machine learning is driving the appetite of the research community for various types of signal data such as patient vitals. Health care systems, however, are ill suited for massive processing of large volumes of data. In addition, due to the sheer magnitude of the data being collected, it is not feasible to retain all of the data in health care systems in perpetuity. This gold mine of information gets purged periodically thereby losing invaluable future research opportunities. We have developed a highly scalable solution that: a) siphons off patient vital data on a nightly basis from on-premises bio-medical systems to a cloud storage location as a permanent archive, b) reconstructs the database in the cloud, c) generates waveforms, alarms and numeric data in a research-ready format, and d) uploads the processed data to a storage location in the cloud ready for research. The data is de-identified and catalogued such that it can be joined with Electronic Medical Records (EMR) and other ancillary data types such as electroencephalogram (EEG), radiology, video monitoring etc. This technique eliminates the research burden from health care systems. This highly scalable solution is used to process high density patient monitoring data aggregated by the Philips Patient Information Center iX (PIC iX) hospital surveillance system for archival storage in the Philips Data Warehouse Connect enterprise-level database. The solution is part of a broader platform that supports a secure high performance clinical data science platform.

中文翻译：

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来自床边监测设备的高度可扩展的波形和生命体征数据存储库

过去十年中成本效益高的云计算的出现以及现代医院环境中高保真临床数据的不断积累正在为转化医学带来新的机遇。机器学习正在推动研究界对各种类型的信号数据（例如患者生命体征）的兴趣。然而，医疗保健系统不适合大量数据的大规模处理。此外，由于所收集数据的数量庞大，永久保留医疗保健系统中的所有数据是不可行的。这个信息金矿会定期清除，从而失去宝贵的未来研究机会。我们开发了一个高度可扩展的解决方案：a) 每晚从本地生物医学系统抽取患者重要数据到云存储位置作为永久存档，b) 在云中重建数据库，c) 在研究中生成波形、警报和数字数据-ready 格式，以及 d) 将处理后的数据上传到云中的存储位置以备研究。对数据进行去标识化和编目，以便将其与电子病历 (EMR) 和其他辅助数据类型（如脑电图 (EEG)、放射学、视频监控等）结合起来。这种技术消除了医疗保健系统的研究负担。这种高度可扩展的解决方案用于处理由飞利浦患者信息中心 iX (PIC iX) 医院监控系统聚合的高密度患者监控数据，以便存档存储在飞利浦数据仓库连接企业级数据库中。该解决方案是支持安全高性能临床数据科学平台的更广泛平台的一部分。