分析临床数据的集中和联合模型
Ruowang Li1, Joseph D Romano2, Yong Chen3
1Department of Computational Biomedicine, Cedars-Sinai Medical Center, Los Angeles, California, USA;
Annual review of biomedical data science
|May 9, 2024
概括
精准医学需要分析各种不同的临床数据集. 本综述比较了数据聚合的集中和联合模型,突出了它们的优点,弱点和疾病理解的未来潜力.
科学领域:
- 临床数据分析临床数据分析
- 精准医学研究研究精准医学研究.
背景情况:
- 精准医学依赖于分析大型,多样化的临床数据集.
- 随着数据复杂性的增加,需要有效的聚合方法来全面了解疾病.
研究的目的:
- 审查和比较用于各种临床数据分析的集中和联合模型.
- 讨论这些数据分析方法的方法,挑战和未来的机会.
主要方法:
- 集中和联合数据模型的比较分析.
- 审查当前的方法和临床数据聚合相关的挑战.
主要成果:
- 集中式模式提供了简单性,但也引发了隐私问题.
- 联合模型增强了隐私,但在分析中引入了复杂性.
- 这两种模型在临床数据集成方面都有明显的优点和缺点.
结论:
- 中央化和联邦化模式之间的选择取决于具体的研究需求和约束.
- 这两种方法的进步对于准确医学和临床数据分析的未来至关重要.
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