Overcoming the Barriers That Obscure the Interlinking and Analysis of Clinical Data Through Harmonization and Incremental Learning

Overcoming the Barriers That Obscure the Interlinking and Analysis of Clinical Data Through Harmonization and Incremental Learning

Goal: To present a framework for data sharing, curation, harmonization and federated data analytics to solve open issues in healthcare, such as, the development of robust disease prediction models. Methods: Data curation is applied to remove data inconsistencies. Lexical and semantic matching method...

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Bibliographic Details
Main Authors: Vasileios C. Pezoulas, Konstantina D. Kourou, Fanis Kalatzis, Themis P. Exarchos, Evi Zampeli, Saviana Gandolfo, Andreas Goules, Chiara Baldini, Fotini Skopouli, Salvatore De Vita, Athanasios G. Tzioufas, Dimitrios I. Fotiadis
Format: Article
Language:English
Published: IEEE 2020-01-01
Series:IEEE Open Journal of Engineering in Medicine and Biology
Subjects:
Data sharing
data curation
data harmonization
federated data analytics
incremental learning
Online Access:https://ieeexplore.ieee.org/document/9037264/

Internet

https://ieeexplore.ieee.org/document/9037264/

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