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From free text to clusters of content in health records: an unsupervised graph partitioning approach

Electronic healthcare records contain large volumes of unstructured data in different forms. Free text constitutes a large portion of such data, yet this source of richly detailed information often remains under-used in practice because of a lack of suitable methodologies to extract interpretable co...

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Detalles Bibliográficos
Autores principales: Altuncu, M. Tarik, Mayer, Erik, Yaliraki, Sophia N., Barahona, Mauricio
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Springer International Publishing 2019
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6400329/
https://www.ncbi.nlm.nih.gov/pubmed/30906850
http://dx.doi.org/10.1007/s41109-018-0109-9