Thermal mapp routing in pharmaceutical products transportation using machine learning approach: a systematic review

Detalhes bibliográficos
Autor(a) principal: Mangini, Clayton Gerber
Data de Publicação: 2021
Outros Autores: Lima, Nilsa Duarte da Silva, Nääs, Irenilza de Alencar
Tipo de documento: Artigo
Idioma: eng
Título da fonte: Research, Society and Development
Texto Completo: https://rsdjournal.org/index.php/rsd/article/view/23665
Resumo: The cold chain is crucial to ensure the quality and effectiveness of transported and stored medicines. For this, it is necessary to carry out the thermal mapping of routes for drugs transported between 15°C and 30°C, so that the most assertive decision can be taken without raising costs. This study aims to identify the main factors influencing the thermal mapping of pharmaceutical products in the cold chain and applying the machine learning technique. The method used for this systematic review is the Prisma, where the identification, screening, eligibility, and inclusion stages were analyzed. After analyzing 75 articles, the result shows that only eight papers were consistent with the use of modeling in the medicine cold chain distribution. Thus, it can be concluded that there is an extensive field to be researched regarding the use of prediction algorithms in the cold chain of drugs and vaccines.
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spelling Thermal mapp routing in pharmaceutical products transportation using machine learning approach: a systematic reviewMapeo térmico de rutas en el transporte de productos farmacéuticos utilizando el enfoque de aprendizaje máquina: una revisión sistemáticaMapeamento térmico de rotas no transporte de produtos farmacêuticos usando a abordagem de aprendizagem da máquina: uma revisão sistemáticaCadena de fríoMedicamentosVacunaModeladoControl de calidad.Cold chainMedicinesVaccineModelingQuality control.Cadeia de frioMedicamentosVacinaModelagemControle de qualidade.The cold chain is crucial to ensure the quality and effectiveness of transported and stored medicines. For this, it is necessary to carry out the thermal mapping of routes for drugs transported between 15°C and 30°C, so that the most assertive decision can be taken without raising costs. This study aims to identify the main factors influencing the thermal mapping of pharmaceutical products in the cold chain and applying the machine learning technique. The method used for this systematic review is the Prisma, where the identification, screening, eligibility, and inclusion stages were analyzed. After analyzing 75 articles, the result shows that only eight papers were consistent with the use of modeling in the medicine cold chain distribution. Thus, it can be concluded that there is an extensive field to be researched regarding the use of prediction algorithms in the cold chain of drugs and vaccines.La cadena de frío es fundamental para garantizar la calidad y eficacia de los medicamentos transportados y almacenados. Para ello, es necesario realizar el mapeo térmico de las rutas de los medicamentos transportados entre 15 ° C y 30 ° C, para que se pueda tomar la decisión más asertiva sin incrementar los costos. Este estudio tiene como objetivo identificar los principales factores que influyen en el mapeo térmico de productos farmacéuticos en la cadena de frío y la aplicación de la técnica de aprendizaje automático. El método utilizado para esta revisión sistemática es el Prisma, donde se analizaron las etapas de identificación, cribado, elegibilidad e inclusión. Después de analizar 75 artículos, el resultado muestra que solo ocho artículos fueron consistentes con el uso de modelos en la distribución de la cadena de frío de los medicamentos. Así, se puede concluir que existe un amplio campo por investigar en cuanto al uso de algoritmos de predicción en la cadena de frío de medicamentos y vacunas.A rede de frio é fundamental para garantir a qualidade e eficácia dos medicamentos transportados e armazenados. Para isso, é necessário realizar o mapeamento térmico das rotas dos medicamentos transportados entre 15 ° C e 30 ° C, para que a decisão mais assertiva seja tomada sem aumento de custos. Este estudo tem como objetivo identificar os principais fatores que influenciam o mapeamento térmico de produtos farmacêuticos na cadeia de frio e a aplicação da técnica de aprendizado de máquina. O método utilizado para esta revisão sistemática é o Prisma, onde foram analisadas as etapas de identificação, triagem, elegibilidade e inclusão. Após análise de 75 artigos, o resultado mostra que apenas oito artigos foram consistentes com o uso de modelagem na distribuição da cadeia de frio de medicamentos. Assim, pode-se concluir que existe um amplo campo a ser pesquisado quanto ao uso de algoritmos de predição na cadeia de frio de medicamentos e vacinas.Research, Society and Development2021-12-13info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionapplication/pdfhttps://rsdjournal.org/index.php/rsd/article/view/2366510.33448/rsd-v10i16.23665Research, Society and Development; Vol. 10 No. 16; e170101623665Research, Society and Development; Vol. 10 Núm. 16; e170101623665Research, Society and Development; v. 10 n. 16; e1701016236652525-3409reponame:Research, Society and Developmentinstname:Universidade Federal de Itajubá (UNIFEI)instacron:UNIFEIenghttps://rsdjournal.org/index.php/rsd/article/view/23665/20962Copyright (c) 2021 Clayton Gerber Mangini; Nilsa Duarte da Silva Lima; Irenilza de Alencar Nääshttps://creativecommons.org/licenses/by/4.0info:eu-repo/semantics/openAccessMangini, Clayton GerberLima, Nilsa Duarte da SilvaNääs, Irenilza de Alencar 2021-12-20T11:03:07Zoai:ojs.pkp.sfu.ca:article/23665Revistahttps://rsdjournal.org/index.php/rsd/indexPUBhttps://rsdjournal.org/index.php/rsd/oairsd.articles@gmail.com2525-34092525-3409opendoar:2024-01-17T09:42:27.871661Research, Society and Development - Universidade Federal de Itajubá (UNIFEI)false
dc.title.none.fl_str_mv Thermal mapp routing in pharmaceutical products transportation using machine learning approach: a systematic review
Mapeo térmico de rutas en el transporte de productos farmacéuticos utilizando el enfoque de aprendizaje máquina: una revisión sistemática
Mapeamento térmico de rotas no transporte de produtos farmacêuticos usando a abordagem de aprendizagem da máquina: uma revisão sistemática
title Thermal mapp routing in pharmaceutical products transportation using machine learning approach: a systematic review
spellingShingle Thermal mapp routing in pharmaceutical products transportation using machine learning approach: a systematic review
Mangini, Clayton Gerber
Cadena de frío
Medicamentos
Vacuna
Modelado
Control de calidad.
Cold chain
Medicines
Vaccine
Modeling
Quality control.
Cadeia de frio
Medicamentos
Vacina
Modelagem
Controle de qualidade.
title_short Thermal mapp routing in pharmaceutical products transportation using machine learning approach: a systematic review
title_full Thermal mapp routing in pharmaceutical products transportation using machine learning approach: a systematic review
title_fullStr Thermal mapp routing in pharmaceutical products transportation using machine learning approach: a systematic review
title_full_unstemmed Thermal mapp routing in pharmaceutical products transportation using machine learning approach: a systematic review
title_sort Thermal mapp routing in pharmaceutical products transportation using machine learning approach: a systematic review
author Mangini, Clayton Gerber
author_facet Mangini, Clayton Gerber
Lima, Nilsa Duarte da Silva
Nääs, Irenilza de Alencar
author_role author
author2 Lima, Nilsa Duarte da Silva
Nääs, Irenilza de Alencar
author2_role author
author
dc.contributor.author.fl_str_mv Mangini, Clayton Gerber
Lima, Nilsa Duarte da Silva
Nääs, Irenilza de Alencar
dc.subject.por.fl_str_mv Cadena de frío
Medicamentos
Vacuna
Modelado
Control de calidad.
Cold chain
Medicines
Vaccine
Modeling
Quality control.
Cadeia de frio
Medicamentos
Vacina
Modelagem
Controle de qualidade.
topic Cadena de frío
Medicamentos
Vacuna
Modelado
Control de calidad.
Cold chain
Medicines
Vaccine
Modeling
Quality control.
Cadeia de frio
Medicamentos
Vacina
Modelagem
Controle de qualidade.
description The cold chain is crucial to ensure the quality and effectiveness of transported and stored medicines. For this, it is necessary to carry out the thermal mapping of routes for drugs transported between 15°C and 30°C, so that the most assertive decision can be taken without raising costs. This study aims to identify the main factors influencing the thermal mapping of pharmaceutical products in the cold chain and applying the machine learning technique. The method used for this systematic review is the Prisma, where the identification, screening, eligibility, and inclusion stages were analyzed. After analyzing 75 articles, the result shows that only eight papers were consistent with the use of modeling in the medicine cold chain distribution. Thus, it can be concluded that there is an extensive field to be researched regarding the use of prediction algorithms in the cold chain of drugs and vaccines.
publishDate 2021
dc.date.none.fl_str_mv 2021-12-13
dc.type.driver.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
format article
status_str publishedVersion
dc.identifier.uri.fl_str_mv https://rsdjournal.org/index.php/rsd/article/view/23665
10.33448/rsd-v10i16.23665
url https://rsdjournal.org/index.php/rsd/article/view/23665
identifier_str_mv 10.33448/rsd-v10i16.23665
dc.language.iso.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv https://rsdjournal.org/index.php/rsd/article/view/23665/20962
dc.rights.driver.fl_str_mv Copyright (c) 2021 Clayton Gerber Mangini; Nilsa Duarte da Silva Lima; Irenilza de Alencar Nääs
https://creativecommons.org/licenses/by/4.0
info:eu-repo/semantics/openAccess
rights_invalid_str_mv Copyright (c) 2021 Clayton Gerber Mangini; Nilsa Duarte da Silva Lima; Irenilza de Alencar Nääs
https://creativecommons.org/licenses/by/4.0
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
dc.publisher.none.fl_str_mv Research, Society and Development
publisher.none.fl_str_mv Research, Society and Development
dc.source.none.fl_str_mv Research, Society and Development; Vol. 10 No. 16; e170101623665
Research, Society and Development; Vol. 10 Núm. 16; e170101623665
Research, Society and Development; v. 10 n. 16; e170101623665
2525-3409
reponame:Research, Society and Development
instname:Universidade Federal de Itajubá (UNIFEI)
instacron:UNIFEI
instname_str Universidade Federal de Itajubá (UNIFEI)
instacron_str UNIFEI
institution UNIFEI
reponame_str Research, Society and Development
collection Research, Society and Development
repository.name.fl_str_mv Research, Society and Development - Universidade Federal de Itajubá (UNIFEI)
repository.mail.fl_str_mv rsd.articles@gmail.com
_version_ 1797052698157121536

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