介绍了BlendedICU数据集,这是第一个协调的,国际密集护理数据集
Matthieu Oliver1, Jérôme Allyn2, Rémi Carencotte1
1Methodological Support Unit, Reunion University Hospital, La Réunion, France; Clinical Informatics Department, Reunion University Hospital, La Réunion, France.
Journal of biomedical informatics
|September 28, 2023
概括
新的BlendedICU数据集结合了国际重症监护单位的数据,揭示了临床实践和结果的重大差异. 这凸显了机器学习对重症监护的普遍性研究的需要.
科学领域:
- 关键护理医学 关键护理医学
- 医疗信息学 医疗信息学
- 机器学习在医疗保健中的应用
背景情况:
- 重症监护室 (ICU) 产生大量复杂的患者数据.
- 现有的数据集往往缺乏机器学习模型的强大概括性研究的规模和多样性.
- 了解不同ICU临床实践的差异对于改善患者护理至关重要.
研究的目的:
- 引入BlendedICU数据集,这是一个大规模的国际密集护理数据收集.
- 促进机器学习模型在重症监护中的通用性研究.
- 为了使不同ICU的临床实践的统计探索.
主要方法:
- 利用了四个公开可用的,患者级重症监护数据库.
- 开发了一个可定制的预处理管道来提取和协调患者变量.
- 标准化数据用于观察医学结果伙伴关系 (OMOP) 共同数据模型.
- 进行了比较分析,以确定源数据库之间的差异.
主要成果:
- 混合ICU数据集包括来自三个国家的309,000多例重症监护入院病例,跨越了13年.
- 包括41个时间序列变量和113个活性成分的暴露数据.
- 在数据收集,药物暴露和患者结局方面,在源数据库中观察到显著的差异.
结论:
- 数据和结果的变化表明ICU之间患者表型和临床实践的差异.
- 证实了机器学习模型在重症监护中的普遍性研究的必要性.
- 为开发高效和可通用的机器学习模型和探索全球ICU临床实践提供了宝贵的资源.
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