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: | Jornal Brasileiro de Nefrologia |
Texto Completo: | http://old.scielo.br/scielo.php?script=sci_arttext&pid=S0101-28002019000200284 |
Resumo: | Abstract Introduction: 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 transplantationMachine LearningKidney TransplantationModels, StatisticalAbstract Introduction: 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.Sociedade Brasileira de Nefrologia2019-06-01info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersiontext/htmlhttp://old.scielo.br/scielo.php?script=sci_arttext&pid=S0101-28002019000200284Brazilian Journal of Nephrology v.41 n.2 2019reponame:Jornal Brasileiro de Nefrologiainstname:Sociedade Brasileira de Nefrologia (SBN)instacron:SBN10.1590/2175-8239-jbn-2018-0047info:eu-repo/semantics/openAccessHannun,Pedro Guilherme CoelhoAndrade,Luis Gustavo Modelli deeng2019-07-29T00:00:00Zoai:scielo:S0101-28002019000200284Revistahttp://www.bjn.org.br/ONGhttps://old.scielo.br/oai/scielo-oai.php||jbn@sbn.org.br2175-82390101-2800opendoar:2019-07-29T00:00Jornal Brasileiro de Nefrologia - Sociedade Brasileira de Nefrologia (SBN)false |
dc.title.none.fl_str_mv |
The future is coming: promising perspectives regarding the use of machine learning in renal transplantation |
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 |
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.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 |
topic |
Machine Learning Kidney Transplantation Models, Statistical |
description |
Abstract Introduction: 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 |
2019-06-01 |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/article |
dc.type.status.fl_str_mv |
info:eu-repo/semantics/publishedVersion |
format |
article |
status_str |
publishedVersion |
dc.identifier.uri.fl_str_mv |
http://old.scielo.br/scielo.php?script=sci_arttext&pid=S0101-28002019000200284 |
url |
http://old.scielo.br/scielo.php?script=sci_arttext&pid=S0101-28002019000200284 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
10.1590/2175-8239-jbn-2018-0047 |
dc.rights.driver.fl_str_mv |
info:eu-repo/semantics/openAccess |
eu_rights_str_mv |
openAccess |
dc.format.none.fl_str_mv |
text/html |
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 |
Brazilian Journal of Nephrology v.41 n.2 2019 reponame:Jornal Brasileiro de Nefrologia instname:Sociedade Brasileira de Nefrologia (SBN) instacron:SBN |
instname_str |
Sociedade Brasileira de Nefrologia (SBN) |
instacron_str |
SBN |
institution |
SBN |
reponame_str |
Jornal Brasileiro de Nefrologia |
collection |
Jornal Brasileiro de Nefrologia |
repository.name.fl_str_mv |
Jornal Brasileiro de Nefrologia - Sociedade Brasileira de Nefrologia (SBN) |
repository.mail.fl_str_mv |
||jbn@sbn.org.br |
_version_ |
1752122065191698432 |