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萨尔茨堡重症监护数据库(SICdb):与 MIMIC-IV 的详细探索和比较分析。

Salzburg Intensive Care database (SICdb): a detailed exploration and comparative analysis with MIMIC-IV.

机构信息

Department for Medical Data Science, Leipzig University Medical Center, Leipzig, Germany.

Institute for Medical Informatics, Statistics and Epidemiology, Leipzig University, Leipzig, Germany.

出版信息

Sci Rep. 2024 May 20;14(1):11438. doi: 10.1038/s41598-024-61380-0.

DOI:10.1038/s41598-024-61380-0
PMID:38763952
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC11102905/
Abstract

The utilization of artificial intelligence (AI) in healthcare is on the rise, demanding increased accessibility to (public) medical data for benchmarking. The digitization of healthcare in recent years has facilitated medical data scientists' access to extensive hospital data, fostering AI-based research. A notable addition to this trend is the Salzburg Intensive Care database (SICdb), made publicly available in early 2023. Covering over 27 thousand intensive care admissions at the University Hospital Salzburg from 2013 to 2021, this dataset presents a valuable resource for AI-driven investigations. This article explores the SICdb and conducts a comparative analysis with the widely recognized Medical Information Mart for Intensive Care - version IV (MIMIC-IV) database. The comparison focuses on key aspects, emphasizing the availability and granularity of data provided by the SICdb, particularly vital signs and laboratory measurements. The analysis demonstrates that the SICdb offers more detailed information with higher data availability and temporal resolution for signal data, especially for vital signs, compared to the MIMIC-IV. This is advantageous for longitudinal studies of patients' health conditions in the intensive care unit. The SICdb provides a valuable resource for medical data scientists and researchers. The database offers comprehensive and diverse healthcare data in a European country, making it well suited for benchmarking and enhancing AI-based healthcare research. The importance of ongoing efforts to expand and make public datasets available for advancing AI applications in the healthcare domain is emphasized by the findings.

摘要

人工智能(AI)在医疗保健领域的应用日益增多,这就需要增加对(公共)医疗数据的访问权限,以进行基准测试。近年来,医疗保健领域的数字化使得医疗数据科学家能够访问广泛的医院数据,从而促进了基于人工智能的研究。这一趋势的一个显著补充是萨尔茨堡重症监护数据库(SICdb),该数据库于 2023 年初公开提供。该数据集涵盖了 2013 年至 2021 年萨尔茨堡大学医院的 2.7 万多例重症监护入院病例,是 AI 驱动研究的宝贵资源。本文探讨了 SICdb,并与广泛认可的重症监护医疗信息集市 - 第四版(MIMIC-IV)数据库进行了比较分析。比较重点关注关键方面,强调了 SICdb 提供的数据的可用性和粒度,特别是重要的生命体征和实验室测量数据。分析表明,与 MIMIC-IV 相比,SICdb 为信号数据,特别是生命体征,提供了更详细的信息,具有更高的数据可用性和时间分辨率。这对于在重症监护病房中对患者健康状况进行纵向研究非常有利。SICdb 为医疗数据科学家和研究人员提供了有价值的资源。该数据库提供了一个全面而多样化的欧洲国家的医疗保健数据,非常适合基准测试和增强基于人工智能的医疗保健研究。研究结果强调了不断努力扩展和公开数据集以推进人工智能在医疗保健领域应用的重要性。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/befb/11102905/8f8127fa3e8f/41598_2024_61380_Fig6_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/befb/11102905/5a9c10434132/41598_2024_61380_Fig1_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/befb/11102905/fe17c2c22ab5/41598_2024_61380_Fig2_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/befb/11102905/9afb3c492fa3/41598_2024_61380_Fig3_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/befb/11102905/aa3f190c74a8/41598_2024_61380_Fig4_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/befb/11102905/e7137b74328c/41598_2024_61380_Fig5_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/befb/11102905/8f8127fa3e8f/41598_2024_61380_Fig6_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/befb/11102905/5a9c10434132/41598_2024_61380_Fig1_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/befb/11102905/fe17c2c22ab5/41598_2024_61380_Fig2_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/befb/11102905/9afb3c492fa3/41598_2024_61380_Fig3_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/befb/11102905/aa3f190c74a8/41598_2024_61380_Fig4_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/befb/11102905/e7137b74328c/41598_2024_61380_Fig5_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/befb/11102905/8f8127fa3e8f/41598_2024_61380_Fig6_HTML.jpg

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本文引用的文献

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2
A guide to sharing open healthcare data under the General Data Protection Regulation.《通用数据保护条例》下开放医疗保健数据共享指南。
Sci Data. 2023 Jun 24;10(1):404. doi: 10.1038/s41597-023-02256-2.
3
The Salzburg Intensive Care database (SICdb): an openly available critical care dataset.萨尔茨堡重症监护数据库(SICdb):一个可公开获取的重症监护数据集。
利用常规收集的重症监护病房数据的力量。
Intensive Care Med. 2025 Jan;51(1):163-166. doi: 10.1007/s00134-024-07745-5. Epub 2024 Dec 11.
Intensive Care Med. 2023 Jun;49(6):700-702. doi: 10.1007/s00134-023-07046-3. Epub 2023 Apr 13.
4
The association between serum albumin and long length of stay of patients with acute heart failure: A retrospective study based on the MIMIC-IV database.血清白蛋白与急性心力衰竭患者住院时间延长的关系:基于 MIMIC-IV 数据库的回顾性研究。
PLoS One. 2023 Feb 24;18(2):e0282289. doi: 10.1371/journal.pone.0282289. eCollection 2023.
5
Global healthcare fairness: We should be sharing more, not less, data.全球医疗公平性:我们应该更多地共享数据,而非更少。
PLOS Digit Health. 2022 Oct 6;1(10):e0000102. doi: 10.1371/journal.pdig.0000102. eCollection 2022 Oct.
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MIMIC-IV, a freely accessible electronic health record dataset.MIMIC-IV,一个可自由访问的电子健康记录数据集。
Sci Data. 2023 Jan 3;10(1):1. doi: 10.1038/s41597-022-01899-x.
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