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Deep learning algorithms with mixed data for prediction of Length of Stay

Detalles Bibliográficos
Autor principal: Falavigna, Greta
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Springer International Publishing 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8043423/
https://www.ncbi.nlm.nih.gov/pubmed/33851300
http://dx.doi.org/10.1007/s11739-021-02736-6
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author Falavigna, Greta
author_facet Falavigna, Greta
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spelling pubmed-80434232021-04-14 Deep learning algorithms with mixed data for prediction of Length of Stay Falavigna, Greta Intern Emerg Med Im - Commentary Springer International Publishing 2021-04-13 2021 /pmc/articles/PMC8043423/ /pubmed/33851300 http://dx.doi.org/10.1007/s11739-021-02736-6 Text en © Società Italiana di Medicina Interna (SIMI) 2021 This article is made available via the PMC Open Access Subset for unrestricted research re-use and secondary analysis in any form or by any means with acknowledgement of the original source. These permissions are granted for the duration of the World Health Organization (WHO) declaration of COVID-19 as a global pandemic.
spellingShingle Im - Commentary
Falavigna, Greta
Deep learning algorithms with mixed data for prediction of Length of Stay
title Deep learning algorithms with mixed data for prediction of Length of Stay
title_full Deep learning algorithms with mixed data for prediction of Length of Stay
title_fullStr Deep learning algorithms with mixed data for prediction of Length of Stay
title_full_unstemmed Deep learning algorithms with mixed data for prediction of Length of Stay
title_short Deep learning algorithms with mixed data for prediction of Length of Stay
title_sort deep learning algorithms with mixed data for prediction of length of stay
topic Im - Commentary
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8043423/
https://www.ncbi.nlm.nih.gov/pubmed/33851300
http://dx.doi.org/10.1007/s11739-021-02736-6
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