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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...
| Autores principales: | , , , |
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| Formato: | Online Artículo Texto |
| Lenguaje: | English |
| Publicado: |
Springer International Publishing
2019
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| 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 |