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33 Forecasting Daily Patient Arrivals during COVID-19 in Emergency Departments: A Deep Learning Approach
| Autores principales: | , , , , , , , |
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| Formato: | Online Artículo Texto |
| Lenguaje: | English |
| Publicado: |
Published by Mosby, Inc.
2021
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| Materias: | |
| Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8536267/ http://dx.doi.org/10.1016/j.annemergmed.2021.09.041 |
| _version_ | 1784587980933955584 |
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| author | Etu, E.-E. Monplaisir, L. Aguwa, C. Arslanturk, S. Masoud, S. Krupp, S. Shih, D. Miller, J. |
| author_facet | Etu, E.-E. Monplaisir, L. Aguwa, C. Arslanturk, S. Masoud, S. Krupp, S. Shih, D. Miller, J. |
| author_sort | Etu, E.-E. |
| collection | PubMed |
| description | |
| format | Online Article Text |
| id | pubmed-8536267 |
| institution | National Center for Biotechnology Information |
| language | English |
| publishDate | 2021 |
| publisher | Published by Mosby, Inc. |
| record_format | MEDLINE/PubMed |
| spelling | pubmed-85362672021-10-25 33 Forecasting Daily Patient Arrivals during COVID-19 in Emergency Departments: A Deep Learning Approach Etu, E.-E. Monplaisir, L. Aguwa, C. Arslanturk, S. Masoud, S. Krupp, S. Shih, D. Miller, J. Ann Emerg Med Research Forum Abstract Published by Mosby, Inc. 2021-10 2021-10-22 /pmc/articles/PMC8536267/ http://dx.doi.org/10.1016/j.annemergmed.2021.09.041 Text en Copyright © 2021 Published by Mosby, Inc. Since January 2020 Elsevier has created a COVID-19 resource centre with free information in English and Mandarin on the novel coronavirus COVID-19. The COVID-19 resource centre is hosted on Elsevier Connect, the company's public news and information website. Elsevier hereby grants permission to make all its COVID-19-related research that is available on the COVID-19 resource centre - including this research content - immediately available in PubMed Central and other publicly funded repositories, such as the WHO COVID database with rights for unrestricted research re-use and analyses in any form or by any means with acknowledgement of the original source. These permissions are granted for free by Elsevier for as long as the COVID-19 resource centre remains active. |
| spellingShingle | Research Forum Abstract Etu, E.-E. Monplaisir, L. Aguwa, C. Arslanturk, S. Masoud, S. Krupp, S. Shih, D. Miller, J. 33 Forecasting Daily Patient Arrivals during COVID-19 in Emergency Departments: A Deep Learning Approach |
| title | 33 Forecasting Daily Patient Arrivals during COVID-19 in Emergency Departments: A Deep Learning Approach |
| title_full | 33 Forecasting Daily Patient Arrivals during COVID-19 in Emergency Departments: A Deep Learning Approach |
| title_fullStr | 33 Forecasting Daily Patient Arrivals during COVID-19 in Emergency Departments: A Deep Learning Approach |
| title_full_unstemmed | 33 Forecasting Daily Patient Arrivals during COVID-19 in Emergency Departments: A Deep Learning Approach |
| title_short | 33 Forecasting Daily Patient Arrivals during COVID-19 in Emergency Departments: A Deep Learning Approach |
| title_sort | 33 forecasting daily patient arrivals during covid-19 in emergency departments: a deep learning approach |
| topic | Research Forum Abstract |
| url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8536267/ http://dx.doi.org/10.1016/j.annemergmed.2021.09.041 |
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