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An Assessment of Federated Machine Learning for Translational Research

An Assessment of Federated Machine Learning for Translational Research

Manoj A. Thomas, Diya Suzanne Abraham, Dapeng Liu
Copyright: © 2020 |Pages: 20
ISBN13: 9781799818793|ISBN10: 1799818799|ISBN13 Softcover: 9781799851486|EISBN13: 9781799818809
DOI: 10.4018/978-1-7998-1879-3.ch006
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MLA

Thomas, Manoj A., et al. "An Assessment of Federated Machine Learning for Translational Research." Interdisciplinary Approaches to Digital Transformation and Innovation, edited by Rocci Luppicini, IGI Global, 2020, pp. 123-142. https://doi.org/10.4018/978-1-7998-1879-3.ch006

APA

Thomas, M. A., Abraham, D. S., & Liu, D. (2020). An Assessment of Federated Machine Learning for Translational Research. In R. Luppicini (Ed.), Interdisciplinary Approaches to Digital Transformation and Innovation (pp. 123-142). IGI Global. https://doi.org/10.4018/978-1-7998-1879-3.ch006

Chicago

Thomas, Manoj A., Diya Suzanne Abraham, and Dapeng Liu. "An Assessment of Federated Machine Learning for Translational Research." In Interdisciplinary Approaches to Digital Transformation and Innovation, edited by Rocci Luppicini, 123-142. Hershey, PA: IGI Global, 2020. https://doi.org/10.4018/978-1-7998-1879-3.ch006

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Abstract

Translational research (TR) is the harnessing of knowledge from basic science and clinical research to advance healthcare. As a sister discipline, translational informatics (TI) concerns the application of informatics theories, methods, and frameworks to TR. This chapter builds upon TR concepts and aims to advance the use of machine learning (ML) and data analytics for improving clinical decision support. A federated machine learning (FML) architecture is described to aggregate multiple ML endpoints, and intermediate data analytic processes and products to output high quality knowledge discovery and decision making. The proposed architecture is evaluated for its operational performance based on three propositions, and a case for clinical decision support in the prediction of adult Sepsis is presented. The chapter illustrates contributions to the advancement of FML and TI.

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