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dc.contributor.authorPuente-Castro, Alejandro
dc.contributor.authorGaldo, Brais
dc.contributor.authorSaid-Criado, Ismael
dc.contributor.authorBaltar Boileve, David
dc.contributor.authorRabuñal, Juan R.
dc.contributor.authorPazos, A.
dc.contributor.authorMartínez Pillado, Modesto
dc.date.accessioned2022-01-03T13:14:09Z
dc.date.available2022-01-03T13:14:09Z
dc.date.issued2021
dc.identifier.citationPuente-Castro, A.; Galdo, B.; Criado, I.S.; Boileve, D.B.; Rabuñal, J.R.; Pazos, A.; Martínez-Pillado, M. PRACTICUM DIRECT Simulator for Decision Making during Pandemics. Eng. Proc. 2021, 7, 48. https://doi.org/10.3390/engproc2021007048es_ES
dc.identifier.urihttp://hdl.handle.net/2183/29302
dc.descriptionPresented at the 4th XoveTIC Conference, A Coruña, Spain, 7–8 October 2021es_ES
dc.description.abstract[Abstract] The past and current situation of the SARS-CoV-2 pandemic has put the entire society, and especially all hospital systems, worldwide to the test. It is essential that health system managers and decision makers optimize the management of resources, even being forced to improvise new units, divert resources usually destined to other functions and/or change the usual care modality by considerably enhancing aspects of telemedicine. Artificial Intelligence (AI) techniques and procedures are of great help in decision making in emergency environments due to severe pandemics because of their predictive capacity. This paper presents the PRACTICUM DIRECT project, which proposes the design and implementation of a tool to assist health system managers in making decisions on the early management of hospital resources. It makes use of AI techniques to identify the most critical variables in each case and build models capable of showing the possibilities and consequences of the decisions taken on resources at each moment of the emergency. It includes a simulator that shows how they would affect management. The current status is that of the selection of the most appropriate variables, taking into account those affected during the SARS-CoV-2 pandemic: infectious diseases, cardio-neuro-circulatory diseases, metabolic diseases and rehabilitative medicine.es_ES
dc.description.sponsorshipThis research was funded by the General Directorate of Culture, Education and University Management of Xunta de Galicia “PRACTICUM DIRECT” Ref. IN845D-2020/03 and the GRANT FOR THE PROGRAM FOR CONSOLIDATION AND STRUCTURING OF COMPETITIVE RESEARCH UNITS Ref. ED431C 2018/49es_ES
dc.description.sponsorshipXunta de Galicia; IN845D-2020/03es_ES
dc.description.sponsorshipXunta de Galicia; ED431C 2018/49
dc.language.isoenges_ES
dc.publisherMDPIes_ES
dc.relation.urihttps://doi.org/10.3390/engproc2021007048es_ES
dc.rightsAtribución 4.0 Internacionales_ES
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/*
dc.subjectPandemicses_ES
dc.subjectArtificial intelligencees_ES
dc.subjectSimulatores_ES
dc.subjectResourceses_ES
dc.subjectExpert systemes_ES
dc.titlePRACTICUM DIRECT Simulator for Decision Making during Pandemicses_ES
dc.typeinfo:eu-repo/semantics/conferenceObjectes_ES
dc.rights.accessinfo:eu-repo/semantics/openAccesses_ES
UDC.journalTitleEngineering Proceedingses_ES
UDC.volume7es_ES
UDC.issue1es_ES
UDC.startPage48es_ES
dc.identifier.doi10.3390/engproc2021007048
UDC.conferenceTitle4th XoveTIC Conferencees_ES


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