The future is coming: promising perspectives regarding the use of machine learning in renal transplantation
Autor(a) principal: | |
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Data de Publicação: | 2019 |
Outros Autores: | |
Tipo de documento: | Artigo |
Idioma: | eng |
Título da fonte: | Repositório Institucional da UNESP |
Texto Completo: | http://dx.doi.org/10.1590/2175-8239-jbn-2018-0047 http://hdl.handle.net/11449/183727 |
Resumo: | AbstractIntroduction: The prediction of post transplantation outcomes is clinically important and involves several problems. The current prediction models based on standard statistics are very complex, difficult to validate and do not provide accurate prediction. Machine learning, a statistical technique that allows the computer to make future predictions using previous experiences, is beginning to be used in order to solve these issues. In the field of kidney transplantation, computational forecasting use has been reported in prediction of chronic allograft rejection, delayed graft function, and graft survival. This paper describes machine learning principles and steps to make a prediction and performs a brief analysis of the most recent applications of its application in literature.Discussion: There is compelling evidence that machine learning approaches based on donor and recipient data are better in providing improved prognosis of graft outcomes than traditional analysis. The immediate expectations that emerge from this new prediction modelling technique are that it will generate better clinical decisions based on dynamic and local practice data and optimize organ allocation as well as post transplantation care management. Despite the promising results, there is no substantial number of studies yet to determine feasibility of its application in a clinical setting.Conclusion: The way we deal with storage data in electronic health records will radically change in the coming years and machine learning will be part of clinical daily routine, whether to predict clinical outcomes or suggest diagnosis based on institutional experience. |
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The future is coming: promising perspectives regarding the use of machine learning in renal transplantationO futuro está chegando: perspectivas promissoras sobre o uso de machine learning no transplante renalMachine LearningKidney TransplantationModels, StatisticalAprendizado de MáquinaTransplante de RimModelos EstatísticosAbstractIntroduction: The prediction of post transplantation outcomes is clinically important and involves several problems. The current prediction models based on standard statistics are very complex, difficult to validate and do not provide accurate prediction. Machine learning, a statistical technique that allows the computer to make future predictions using previous experiences, is beginning to be used in order to solve these issues. In the field of kidney transplantation, computational forecasting use has been reported in prediction of chronic allograft rejection, delayed graft function, and graft survival. This paper describes machine learning principles and steps to make a prediction and performs a brief analysis of the most recent applications of its application in literature.Discussion: There is compelling evidence that machine learning approaches based on donor and recipient data are better in providing improved prognosis of graft outcomes than traditional analysis. The immediate expectations that emerge from this new prediction modelling technique are that it will generate better clinical decisions based on dynamic and local practice data and optimize organ allocation as well as post transplantation care management. Despite the promising results, there is no substantial number of studies yet to determine feasibility of its application in a clinical setting.Conclusion: The way we deal with storage data in electronic health records will radically change in the coming years and machine learning will be part of clinical daily routine, whether to predict clinical outcomes or suggest diagnosis based on institutional experience.ResumoIntrodução: A predição de resultados pós-transplante é clinicamente importante e envolve vários problemas. Os atuais modelos de previsão baseados em padrões estatísticos são muito complexos, difíceis de validar e não fornecem previsões precisas. Machine Learning, é uma técnica estatística que permite que o computador faça previsões futuras usando experiências anteriores, está começando a ser usada para resolver essas questões. No campo do transplante renal, o uso da previsão computacional foi relatado na predição de rejeição crônica de aloenxerto, função tardia do enxerto e sobrevida do enxerto. Este artigo descreve os princípios e etapas de machine learning para fazer uma previsão e realiza uma breve análise das aplicações mais recentes de seu uso na literatura.Discussão: Existem evidências convincentes de que as abordagens de machine learning baseadas nos dados do doador e do receptor são melhores para proporcionar melhor prognóstico dos resultados do enxerto do que a análise tradicional. As expectativas imediatas que emergem dessa nova técnica de modelagem de previsão são que ela gerará melhores decisões clínicas baseadas em dados de práticas dinâmicas e locais e aperfeiçoará a alocação de órgãos, bem como o gerenciamento de cuidados pós-transplante. Apesar dos resultados promissores, ainda não há um número substancial de estudos para determinar a viabilidade de sua aplicação em um cenário clínico.Conclusão: A forma como lidamos com dados de armazenamento em prontuários eletrônicos de saúde mudará radicalmente nos próximos anos e a machine learning fará parte da rotina clínica diária, seja para prever resultados clínicos ou sugerir um diagnóstico baseado na experiência institucional.Universidade Estadual Paulista Departamento de Medicina InternaUniversidade Estadual Paulista Departamento de Medicina InternaSociedade Brasileira de NefrologiaUniversidade Estadual Paulista (Unesp)Hannun, Pedro Guilherme CoelhoAndrade, Luis Gustavo Modelli De2019-10-03T17:31:23Z2019-10-03T17:31:23Z2019-06-01info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/article284-287application/pdfhttp://dx.doi.org/10.1590/2175-8239-jbn-2018-0047Brazilian Journal of Nephrology. Sociedade Brasileira de Nefrologia, v. 41, n. 2, p. 284-287, 2019.0101-2800http://hdl.handle.net/11449/18372710.1590/2175-8239-jbn-2018-0047S0101-28002019000200284S0101-28002019000200284.pdfSciELOreponame:Repositório Institucional da UNESPinstname:Universidade Estadual Paulista (UNESP)instacron:UNESPengBrazilian Journal of Nephrologyinfo:eu-repo/semantics/openAccess2023-11-10T06:16:06Zoai:repositorio.unesp.br:11449/183727Repositório InstitucionalPUBhttp://repositorio.unesp.br/oai/requestopendoar:29462023-11-10T06:16:06Repositório Institucional da UNESP - Universidade Estadual Paulista (UNESP)false |
dc.title.none.fl_str_mv |
The future is coming: promising perspectives regarding the use of machine learning in renal transplantation O futuro está chegando: perspectivas promissoras sobre o uso de machine learning no transplante renal |
title |
The future is coming: promising perspectives regarding the use of machine learning in renal transplantation |
spellingShingle |
The future is coming: promising perspectives regarding the use of machine learning in renal transplantation Hannun, Pedro Guilherme Coelho Machine Learning Kidney Transplantation Models, Statistical Aprendizado de Máquina Transplante de Rim Modelos Estatísticos |
title_short |
The future is coming: promising perspectives regarding the use of machine learning in renal transplantation |
title_full |
The future is coming: promising perspectives regarding the use of machine learning in renal transplantation |
title_fullStr |
The future is coming: promising perspectives regarding the use of machine learning in renal transplantation |
title_full_unstemmed |
The future is coming: promising perspectives regarding the use of machine learning in renal transplantation |
title_sort |
The future is coming: promising perspectives regarding the use of machine learning in renal transplantation |
author |
Hannun, Pedro Guilherme Coelho |
author_facet |
Hannun, Pedro Guilherme Coelho Andrade, Luis Gustavo Modelli De |
author_role |
author |
author2 |
Andrade, Luis Gustavo Modelli De |
author2_role |
author |
dc.contributor.none.fl_str_mv |
Universidade Estadual Paulista (Unesp) |
dc.contributor.author.fl_str_mv |
Hannun, Pedro Guilherme Coelho Andrade, Luis Gustavo Modelli De |
dc.subject.por.fl_str_mv |
Machine Learning Kidney Transplantation Models, Statistical Aprendizado de Máquina Transplante de Rim Modelos Estatísticos |
topic |
Machine Learning Kidney Transplantation Models, Statistical Aprendizado de Máquina Transplante de Rim Modelos Estatísticos |
description |
AbstractIntroduction: The prediction of post transplantation outcomes is clinically important and involves several problems. The current prediction models based on standard statistics are very complex, difficult to validate and do not provide accurate prediction. Machine learning, a statistical technique that allows the computer to make future predictions using previous experiences, is beginning to be used in order to solve these issues. In the field of kidney transplantation, computational forecasting use has been reported in prediction of chronic allograft rejection, delayed graft function, and graft survival. This paper describes machine learning principles and steps to make a prediction and performs a brief analysis of the most recent applications of its application in literature.Discussion: There is compelling evidence that machine learning approaches based on donor and recipient data are better in providing improved prognosis of graft outcomes than traditional analysis. The immediate expectations that emerge from this new prediction modelling technique are that it will generate better clinical decisions based on dynamic and local practice data and optimize organ allocation as well as post transplantation care management. Despite the promising results, there is no substantial number of studies yet to determine feasibility of its application in a clinical setting.Conclusion: The way we deal with storage data in electronic health records will radically change in the coming years and machine learning will be part of clinical daily routine, whether to predict clinical outcomes or suggest diagnosis based on institutional experience. |
publishDate |
2019 |
dc.date.none.fl_str_mv |
2019-10-03T17:31:23Z 2019-10-03T17:31:23Z 2019-06-01 |
dc.type.status.fl_str_mv |
info:eu-repo/semantics/publishedVersion |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/article |
format |
article |
status_str |
publishedVersion |
dc.identifier.uri.fl_str_mv |
http://dx.doi.org/10.1590/2175-8239-jbn-2018-0047 Brazilian Journal of Nephrology. Sociedade Brasileira de Nefrologia, v. 41, n. 2, p. 284-287, 2019. 0101-2800 http://hdl.handle.net/11449/183727 10.1590/2175-8239-jbn-2018-0047 S0101-28002019000200284 S0101-28002019000200284.pdf |
url |
http://dx.doi.org/10.1590/2175-8239-jbn-2018-0047 http://hdl.handle.net/11449/183727 |
identifier_str_mv |
Brazilian Journal of Nephrology. Sociedade Brasileira de Nefrologia, v. 41, n. 2, p. 284-287, 2019. 0101-2800 10.1590/2175-8239-jbn-2018-0047 S0101-28002019000200284 S0101-28002019000200284.pdf |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
Brazilian Journal of Nephrology |
dc.rights.driver.fl_str_mv |
info:eu-repo/semantics/openAccess |
eu_rights_str_mv |
openAccess |
dc.format.none.fl_str_mv |
284-287 application/pdf |
dc.publisher.none.fl_str_mv |
Sociedade Brasileira de Nefrologia |
publisher.none.fl_str_mv |
Sociedade Brasileira de Nefrologia |
dc.source.none.fl_str_mv |
SciELO reponame:Repositório Institucional da UNESP instname:Universidade Estadual Paulista (UNESP) instacron:UNESP |
instname_str |
Universidade Estadual Paulista (UNESP) |
instacron_str |
UNESP |
institution |
UNESP |
reponame_str |
Repositório Institucional da UNESP |
collection |
Repositório Institucional da UNESP |
repository.name.fl_str_mv |
Repositório Institucional da UNESP - Universidade Estadual Paulista (UNESP) |
repository.mail.fl_str_mv |
|
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1799964888908431360 |