在五个ICU数据库中对输血患者进行比较联合分析:使用Kullback-Leibler分歧
Johanna Schwinn1, Seyedmostafa Sheikhalishahi1, Matthaeus Morhart1
1Digital Medicine, University Hospital of Augsburg, Augsburg, Germany.
Studies in health technology and informatics
|October 3, 2025
概括
重症监护数据库中的数据异质性差异很大,影响了联合学习. 输血状态持续增加所有分析数据集的特征分布差异.
科学领域:
- 医疗信息学 医疗信息学
- 数据科学数据科学数据科学
- 临床研究 临床研究
背景情况:
- 医疗保健中的联合学习 (FL) 需要了解机构间的数据分布.
- 重症监护室 (ICU) 数据库的多样性,可能会影响FL模型的概括性.
- 评估数据库之间的特征异质性对于强大的FL部署至关重要.
研究的目的:
- 在五个重症监护数据库中量化特征分布差异.
- 评估输血状态对数据异质性的影响.
- 为在多机构ICU环境中应用联合学习模型提供信息.
主要方法:
- 使用Kullback-Leibler分歧 (KLD) 来测量特征分布差异.
- 在个别数据库和其他数据库的组合之间分析了双向KLD.
- 根据患者输血状态进行分层分析.
主要成果:
- 在数据库中观察到显著的异质性 (HiRID,UKA,MIMIC-IV).
- HiRID显示出最大的分歧,特别是在输血病例中.
- 在所有数据集中,输血病例始终显示出比非输血病例的差异更高.
结论:
- 机构特定的做法有助于ICU数据库中的数据异质性.
- 在实施前对数据异质性的评估对于联合学习至关重要.
- 结果突出了使用FL的多数据库ICU研究面临的挑战和考虑因素.
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