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https://hdl.handle.net/1822/89462
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Campo DC | Valor | Idioma |
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dc.contributor.author | Passos, Maria | por |
dc.contributor.author | Duarte, Júlio Miguel Marques | por |
dc.contributor.author | Silva, Alvaro | por |
dc.contributor.author | Manuel, Maria | por |
dc.contributor.author | Quintas, Cesar | por |
dc.date.accessioned | 2024-03-12T18:31:46Z | - |
dc.date.available | 2024-03-12T18:31:46Z | - |
dc.date.issued | 2022 | - |
dc.identifier.citation | Passos, M., Duarte, J., Silva, Á., Manuel, M., & Quintas, C. (2022). Decision models on therapies for intensive medicine. Procedia Computer Science. Elsevier BV. http://doi.org/10.1016/j.procs.2022.10.142 | por |
dc.identifier.issn | 1877-0509 | - |
dc.identifier.uri | https://hdl.handle.net/1822/89462 | - |
dc.description.abstract | Decision support models are crucial in intensive care units as they allow intensivists to make faster and better decisions. The application of optimization models in these areas becomes challenging given its complexity and multidisciplinary nature. The main objective of this study is to use the stochastic Hill Climbing optimization model in order to identify the best medication to treat the Covid Pneumonia problem, considering the top 3 medications administered as well as the cost of treatment. It should be noted that the problem to be analyzed in the optimization model was selected considering that the extracted data is from the time when Covid-19 was ravaging the intensive care units, so it will be the most interesting. The results obtained in this study denote that the n_iterations parameter was crucial in obtaining the optimal solution since all scenarios with this parameter set to a value of 1000 were able to return the optimal solution, unlike the other ones. | por |
dc.description.sponsorship | The work has been supported by FCT – Fundação para a Ciência e Tecnologia within the Project Scope: DSAIPA/DS/0084/2018. | por |
dc.language.iso | eng | por |
dc.publisher | Elsevier 1 | por |
dc.relation | info:eu-repo/grantAgreement/FCT/3599-PPCDT/DSAIPA%2FDS%2F0084%2F2018/PT | por |
dc.rights | openAccess | por |
dc.rights.uri | http://creativecommons.org/licenses/by-nc-nd/4.0/ | por |
dc.subject | Decision Support Systems | por |
dc.subject | Intensive Care Units | por |
dc.subject | Intensive Medicine | por |
dc.subject | Optimization Techniques | por |
dc.subject | Therapies | por |
dc.title | Decision models on therapies for intensive medicine | por |
dc.type | conferencePaper | por |
dc.peerreviewed | yes | por |
dc.relation.publisherversion | https://www.sciencedirect.com/science/article/pii/S187705092201599X | por |
oaire.citationStartPage | 230 | por |
oaire.citationEndPage | 235 | por |
oaire.citationIssue | C | por |
oaire.citationVolume | 210 | por |
dc.date.updated | 2024-03-08T12:27:25Z | - |
dc.identifier.doi | 10.1016/j.procs.2022.10.142 | por |
sdum.export.identifier | 13353 | - |
sdum.journal | Procedia Computer Science | por |
sdum.conferencePublication | Procedia Computer Science | por |
oaire.version | VoR | por |
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Ficheiro | Descrição | Tamanho | Formato | |
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hodii22_passos.pdf | 489,12 kB | Adobe PDF | Ver/Abrir |
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