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A graph based recommender system for managing Covid-19 Crisis

The paper aims to present a graph based recommender system for managing the Covid-19 crisis by considering patient and medical staff data. Working with limited number of medical staff, require optimization when creating the appropriate medical staff to assist patient. Patient medical files usually c...

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Detalles Bibliográficos
Autores principales: Sayeb, Yemna, Jebri, Marwa, Ghezala, Henda Ben
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Published by Elsevier B.V. 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8745932/
https://www.ncbi.nlm.nih.gov/pubmed/35035620
http://dx.doi.org/10.1016/j.procs.2021.12.023
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author Sayeb, Yemna
Jebri, Marwa
Ghezala, Henda Ben
author_facet Sayeb, Yemna
Jebri, Marwa
Ghezala, Henda Ben
author_sort Sayeb, Yemna
collection PubMed
description The paper aims to present a graph based recommender system for managing the Covid-19 crisis by considering patient and medical staff data. Working with limited number of medical staff, require optimization when creating the appropriate medical staff to assist patient. Patient medical files usually contain more information about the patient diseases and symptoms. In this paper the recommender system at first analyses the patient medical files to find and decide which profile of medical staff could assist efficiency this patient in a crisis situation. Second the recommender system by taking into account the availability of the medical staff will try to propose others doctors with the same profile and the nearest competencies.
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spelling pubmed-87459322022-01-10 A graph based recommender system for managing Covid-19 Crisis Sayeb, Yemna Jebri, Marwa Ghezala, Henda Ben Procedia Comput Sci Article The paper aims to present a graph based recommender system for managing the Covid-19 crisis by considering patient and medical staff data. Working with limited number of medical staff, require optimization when creating the appropriate medical staff to assist patient. Patient medical files usually contain more information about the patient diseases and symptoms. In this paper the recommender system at first analyses the patient medical files to find and decide which profile of medical staff could assist efficiency this patient in a crisis situation. Second the recommender system by taking into account the availability of the medical staff will try to propose others doctors with the same profile and the nearest competencies. Published by Elsevier B.V. 2022 2022-01-10 /pmc/articles/PMC8745932/ /pubmed/35035620 http://dx.doi.org/10.1016/j.procs.2021.12.023 Text en © 2021 Published by Elsevier B.V. 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 Article
Sayeb, Yemna
Jebri, Marwa
Ghezala, Henda Ben
A graph based recommender system for managing Covid-19 Crisis
title A graph based recommender system for managing Covid-19 Crisis
title_full A graph based recommender system for managing Covid-19 Crisis
title_fullStr A graph based recommender system for managing Covid-19 Crisis
title_full_unstemmed A graph based recommender system for managing Covid-19 Crisis
title_short A graph based recommender system for managing Covid-19 Crisis
title_sort graph based recommender system for managing covid-19 crisis
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8745932/
https://www.ncbi.nlm.nih.gov/pubmed/35035620
http://dx.doi.org/10.1016/j.procs.2021.12.023
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