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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Detalles Bibliográficos
Formato: Online Artículo Texto
Lenguaje:English
Publicado: IEEE 2020
Materias:
Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8940202/
https://www.ncbi.nlm.nih.gov/pubmed/35402941
http://dx.doi.org/10.1109/OJEMB.2020.2981258

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8940202/
https://www.ncbi.nlm.nih.gov/pubmed/35402941
http://dx.doi.org/10.1109/OJEMB.2020.2981258

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