Academic Journal
From intensive care monitors to cloud environments: a structured data pipeline for advanced clinical decision supportResearch in context
| Title: | From intensive care monitors to cloud environments: a structured data pipeline for advanced clinical decision supportResearch in context |
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| Authors: | Sijm H. Noteboom, Eline Kho, Maria Galanty, Clara I. Sánchez, Frans C.P. ten Bookum, Denise P. Veelo, Alexander P.J. Vlaar, Björn J.P. van der Ster |
| Source: | EBioMedicine, Vol 111, Iss , Pp 105529- (2025) |
| Publisher Information: | Elsevier, 2025. |
| Publication Year: | 2025 |
| Collection: | LCC:Medicine LCC:Medicine (General) |
| Subject Terms: | Cloud environments, Data-driven algorithms, Data management, Real-time decision-making, Medicine, Medicine (General), R5-920 |
| More Details: | Summary: Background: 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. Methods: 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. Findings: 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. Interpretation: The combined data and features of cloud environments offer support for predictive algorithm development and implementation, healthcare evaluation, and improved individual patient care. Funding: University of Amsterdam Research Priority Agenda Program AI for Heath Decision-Making. |
| Document Type: | article |
| File Description: | electronic resource |
| Language: | English |
| ISSN: | 2352-3964 |
| Relation: | http://www.sciencedirect.com/science/article/pii/S2352396424005656; https://doaj.org/toc/2352-3964 |
| DOI: | 10.1016/j.ebiom.2024.105529 |
| Access URL: | https://doaj.org/article/09cace27fc7e42fcba52708c939cc08d |
| Accession Number: | edsdoj.09cace27fc7e42fcba52708c939cc08d |
| Database: | Directory of Open Access Journals |
| ISSN: | 23523964 |
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| DOI: | 10.1016/j.ebiom.2024.105529 |
| Published in: | EBioMedicine |
| Language: | English |