Centralized and Federated Models for the Analysis of Clinical Data.

IF 7 Q1 MATHEMATICAL & COMPUTATIONAL BIOLOGY
Ruowang Li, Joseph D Romano, Yong Chen, Jason H Moore
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引用次数: 0

Abstract

The progress of precision medicine research hinges on the gathering and analysis of extensive and diverse clinical datasets. With the continued expansion of modalities, scales, and sources of clinical datasets, it becomes imperative to devise methods for aggregating information from these varied sources to achieve a comprehensive understanding of diseases. In this review, we describe two important approaches for the analysis of diverse clinical datasets, namely the centralized model and federated model. We compare and contrast the strengths and weaknesses inherent in each model and present recent progress in methodologies and their associated challenges. Finally, we present an outlook on the opportunities that both models hold for the future analysis of clinical data.

临床数据分析的集中模式和联合模式。
精准医学研究的进展取决于对广泛而多样的临床数据集的收集和分析。随着临床数据集的模式、规模和来源的不断扩大,当务之急是设计出从这些不同来源汇总信息的方法,以实现对疾病的全面了解。在这篇综述中,我们介绍了分析多样化临床数据集的两种重要方法,即集中模式和联合模式。我们比较和对比了每种模式固有的优缺点,并介绍了方法论的最新进展及其相关挑战。最后,我们展望了这两种模式为未来临床数据分析带来的机遇。
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来源期刊
CiteScore
11.10
自引率
1.70%
发文量
0
期刊介绍: The Annual Review of Biomedical Data Science provides comprehensive expert reviews in biomedical data science, focusing on advanced methods to store, retrieve, analyze, and organize biomedical data and knowledge. The scope of the journal encompasses informatics, computational, artificial intelligence (AI), and statistical approaches to biomedical data, including the sub-fields of bioinformatics, computational biology, biomedical informatics, clinical and clinical research informatics, biostatistics, and imaging informatics. The mission of the journal is to identify both emerging and established areas of biomedical data science, and the leaders in these fields.
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