Centralized and Federated Models for the Analysis of Clinical Data

Author:

Li Ruowang1,Romano Joseph D.2,Chen Yong3,Moore Jason H.1

Affiliation:

1. 1Department of Computational Biomedicine, Cedars-Sinai Medical Center, Los Angeles, California, USA; email: Jason.Moore@csmc.edu

2. 2Institute for Biomedical Informatics, University of Pennsylvania, Philadelphia, Pennsylvania, USA

3. 3Department of Biostatistics, Epidemiology, and Informatics, University of Pennsylvania, Philadelphia, Pennsylvania, USA

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.

Publisher

Annual Reviews

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