Noteboom Sijm H, Kho Eline, Galanty Maria, Sánchez Clara I, Ten Bookum Frans C P, Veelo Denise P, Vlaar Alexander P J, van der Ster Björn J P
Department of Anaesthesiology, Amsterdam UMC, University of Amsterdam, Meibergdreef 9, 1105 AZ, Amsterdam, the Netherlands; Department of Intensive Care, Amsterdam UMC, University of Amsterdam, Meibergdreef 9, 1105 AZ, Amsterdam, the Netherlands.
Informatics Institute, University of Amsterdam, Meibergdreef 9, 1105 AZ, Amsterdam, the Netherlands.
EBioMedicine. 2025 Jan;111:105529. doi: 10.1016/j.ebiom.2024.105529. Epub 2024 Dec 27.
Clinical decision-making is increasingly shifting towards data-driven approaches and requires large databases to develop state-of-the-art algorithms for diagnosing, detecting and predicting diseases. The intensive care unit (ICU), a data-rich setting, faces challenges with high-frequency, unstructured monitor data. Here, we showcase a successful example of a data pipeline to efficiently move patient data to the cloud environment for structured storage. This supports individual patient analysis, enables largescale retrospective research, and the development of data-driven algorithms.
Since June 2021, ICU data of the Amsterdam UMC have been collected and stored in a third-party cloud environment which is hosted on large virtual servers. The feasibility of the pipeline will be demonstrated with the available data through research and clinical use cases. Furthermore, privacy, safety, data quality, and environmental impact are carefully considered in the cloud storage transition.
Over two years, data from over 9000 patients have been stored in the cloud. The availability, agility, computational power, high uptime, and streaming data pipelines allow for large retrospective analyses as well as the opportunity to implement real-time prediction of critical events with machine learning algorithms. Critical events can be accessed by applying keyword search in the natural language data, annotated by the treating team. Besides, the cloud environment offers storage of institutional data enabling evaluation of healthcare.
The combined data and features of cloud environments offer support for predictive algorithm development and implementation, healthcare evaluation, and improved individual patient care.
University of Amsterdam Research Priority Agenda Program AI for Heath Decision-Making.
临床决策正日益转向数据驱动的方法，需要大型数据库来开发用于疾病诊断、检测和预测的先进算法。重症监护病房（ICU）是一个数据丰富的环境，但面临着高频、非结构化监测数据的挑战。在此，我们展示了一个成功的数据管道示例，可有效地将患者数据移动到云环境中进行结构化存储。这支持对个体患者进行分析，实现大规模回顾性研究以及开发数据驱动的算法。
自2021年6月以来，阿姆斯特丹大学医学中心的ICU数据已被收集并存储在由大型虚拟服务器托管的第三方云环境中。将通过研究和临床用例，利用现有数据来证明该管道的可行性。此外，在云存储转换过程中，会仔细考虑隐私、安全、数据质量和环境影响。
在两年多的时间里，来自9000多名患者的数据已存储在云端。其可用性、灵活性、计算能力、高正常运行时间以及流数据管道允许进行大规模回顾性分析，并有机会通过机器学习算法对关键事件进行实时预测。通过在治疗团队注释的自然语言数据中应用关键词搜索，可以访问关键事件。此外，云环境提供机构数据存储，有助于医疗保健评估。
云环境的综合数据和功能为预测算法的开发与实施、医疗保健评估以及改善个体患者护理提供了支持。
阿姆斯特丹大学健康决策人工智能研究优先议程项目
