A predictive model of mortality in acute renal failure in the critical patient

Background and Aims: Patients with Acute Renal Failure (ARF) have a high risk of mortality, especially those who enter the Intensive Care Unit (ICU). In this population, predictive models of mortality on prognostic scales, such as SAPS-II (Simplified Acute Physiology Score II), linearly relate risk...

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Autores: Ibeas, Jose|||0000-0002-1292-7271, Lleal, Elia, Macias Toro, Edwar Hernando|||0000-0001-5568-9237, Rubiella, Carolina|||0000-0002-6496-8710, Morell, Antoni|||0000-0003-2249-8594, Serrano, Javier|||0000-0003-1235-2145, Lopez Vicario, Jose|||0000-0002-3574-4697
Formato: artículo
Fecha de publicación:2020
País:España
Recursos:Universitat Autònoma de Barcelona
Repositorio:Dipòsit Digital de Documents de la UAB
Idioma:inglés
OAI Identifier:oai:ddd.uab.cat:273483
Acesso em linha:https://ddd.uab.cat/record/273483
https://dx.doi.org/urn:doi:10.1093/ndt/gfaa139.SO019
Access Level:acceso abierto
Palavra-chave:SDG 3 - Good Health and Well-being
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spelling A predictive model of mortality in acute renal failure in the critical patientusefulness of artificial intelligenceIbeas, Jose|||0000-0002-1292-7271Lleal, EliaMacias Toro, Edwar Hernando|||0000-0001-5568-9237Rubiella, Carolina|||0000-0002-6496-8710Morell, Antoni|||0000-0003-2249-8594Serrano, Javier|||0000-0003-1235-2145Lopez Vicario, Jose|||0000-0002-3574-4697SDG 3 - Good Health and Well-beingBackground and Aims: Patients with Acute Renal Failure (ARF) have a high risk of mortality, especially those who enter the Intensive Care Unit (ICU). In this population, predictive models of mortality on prognostic scales, such as SAPS-II (Simplified Acute Physiology Score II), linearly relate risk factors without taking into account the complex relationship's variables can have. There are models where Machine Learning (ML) techniques have been used, but there is still room for improvement. The implementation of deep artificial neural networks (DANN) can be challenging. The literature models, using SAPS-II report an accuracy, f1 and ROC area (receiver operating curve) in ranges of 0.538-0.621, 0.333-0.377 and 0.781-0.809 respectively. The best results with ML are improved in neural networks of a hidden layer or random forest, being the best performance in the latter: accuracy 0.715-0.741, F1 0.449-0.470 and ROC between 0.862-0.870. The aim is to evaluate and improve the predictive capacity of ML techniques for the prediction of mortality in patients with ARF admitted to the ICU, through the use of the open database MIMIC-III (Medical Information Martfor Intensive Care III). Method: Design: Retrospective analysis of historical cooperation of 20,928 patients with ARF from Beth Israel Deaconess (Boston), from 2001 to 2012. Method: ML algorithm based on DANN. Creation of a model to predict in-hospital mortality after discharge from the ICU with the variables of the first 24 hours after admission to the ICU. To evaluate the robustness of the model has been performed cross-validation by separating the samples into different combinations of training and test data (k-folds). The unavailable variables haematological with means extracted from the training set of the respective fold. The DANN trained with the complex relationship's variables folds, two hidden layers of 75 and 40 neurons respectively. Inclusion criteria:. 22020-01-0120202020-01-01Articlehttp://purl.org/coar/resource_type/c_6501AMhttp://purl.org/coar/version/c_ab4af688f83e57aainfo:eu-repo/semantics/articleapplication/pdfhttps://ddd.uab.cat/record/273483https://dx.doi.org/urn:doi:10.1093/ndt/gfaa139.SO019reponame:Dipòsit Digital de Documents de la UABinstname:Universitat Autònoma de BarcelonaInglésengopen accesshttp://purl.org/coar/access_right/c_abf2Aquest material està protegit per drets d'autor i/o drets afins. Podeu utilitzar aquest material en funció del que permet la legislació de drets d'autor i drets afins d'aplicació al vostre cas. Per a d'altres usos heu d'obtenir permís del(s) titular(s) de drets.https://rightsstatements.org/vocab/InC/1.0/info:eu-repo/semantics/openAccessoai:ddd.uab.cat:2734832026-06-06T12:50:31Z
dc.title.none.fl_str_mv A predictive model of mortality in acute renal failure in the critical patient
usefulness of artificial intelligence
title A predictive model of mortality in acute renal failure in the critical patient
spellingShingle A predictive model of mortality in acute renal failure in the critical patient
Ibeas, Jose|||0000-0002-1292-7271
SDG 3 - Good Health and Well-being
title_short A predictive model of mortality in acute renal failure in the critical patient
title_full A predictive model of mortality in acute renal failure in the critical patient
title_fullStr A predictive model of mortality in acute renal failure in the critical patient
title_full_unstemmed A predictive model of mortality in acute renal failure in the critical patient
title_sort A predictive model of mortality in acute renal failure in the critical patient
dc.creator.none.fl_str_mv Ibeas, Jose|||0000-0002-1292-7271
Lleal, Elia
Macias Toro, Edwar Hernando|||0000-0001-5568-9237
Rubiella, Carolina|||0000-0002-6496-8710
Morell, Antoni|||0000-0003-2249-8594
Serrano, Javier|||0000-0003-1235-2145
Lopez Vicario, Jose|||0000-0002-3574-4697
author Ibeas, Jose|||0000-0002-1292-7271
author_facet Ibeas, Jose|||0000-0002-1292-7271
Lleal, Elia
Macias Toro, Edwar Hernando|||0000-0001-5568-9237
Rubiella, Carolina|||0000-0002-6496-8710
Morell, Antoni|||0000-0003-2249-8594
Serrano, Javier|||0000-0003-1235-2145
Lopez Vicario, Jose|||0000-0002-3574-4697
author_role author
author2 Lleal, Elia
Macias Toro, Edwar Hernando|||0000-0001-5568-9237
Rubiella, Carolina|||0000-0002-6496-8710
Morell, Antoni|||0000-0003-2249-8594
Serrano, Javier|||0000-0003-1235-2145
Lopez Vicario, Jose|||0000-0002-3574-4697
author2_role author
author
author
author
author
author
dc.subject.none.fl_str_mv SDG 3 - Good Health and Well-being
topic SDG 3 - Good Health and Well-being
description Background and Aims: Patients with Acute Renal Failure (ARF) have a high risk of mortality, especially those who enter the Intensive Care Unit (ICU). In this population, predictive models of mortality on prognostic scales, such as SAPS-II (Simplified Acute Physiology Score II), linearly relate risk factors without taking into account the complex relationship's variables can have. There are models where Machine Learning (ML) techniques have been used, but there is still room for improvement. The implementation of deep artificial neural networks (DANN) can be challenging. The literature models, using SAPS-II report an accuracy, f1 and ROC area (receiver operating curve) in ranges of 0.538-0.621, 0.333-0.377 and 0.781-0.809 respectively. The best results with ML are improved in neural networks of a hidden layer or random forest, being the best performance in the latter: accuracy 0.715-0.741, F1 0.449-0.470 and ROC between 0.862-0.870. The aim is to evaluate and improve the predictive capacity of ML techniques for the prediction of mortality in patients with ARF admitted to the ICU, through the use of the open database MIMIC-III (Medical Information Martfor Intensive Care III). Method: Design: Retrospective analysis of historical cooperation of 20,928 patients with ARF from Beth Israel Deaconess (Boston), from 2001 to 2012. Method: ML algorithm based on DANN. Creation of a model to predict in-hospital mortality after discharge from the ICU with the variables of the first 24 hours after admission to the ICU. To evaluate the robustness of the model has been performed cross-validation by separating the samples into different combinations of training and test data (k-folds). The unavailable variables haematological with means extracted from the training set of the respective fold. The DANN trained with the complex relationship's variables folds, two hidden layers of 75 and 40 neurons respectively. Inclusion criteria:.
publishDate 2020
dc.date.none.fl_str_mv 2
2020-01-01
2020
2020-01-01
dc.type.none.fl_str_mv Article
http://purl.org/coar/resource_type/c_6501
AM
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dc.type.openaire.fl_str_mv info:eu-repo/semantics/article
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dc.identifier.none.fl_str_mv https://ddd.uab.cat/record/273483
https://dx.doi.org/urn:doi:10.1093/ndt/gfaa139.SO019
url https://ddd.uab.cat/record/273483
https://dx.doi.org/urn:doi:10.1093/ndt/gfaa139.SO019
dc.language.none.fl_str_mv Inglés
eng
language_invalid_str_mv Inglés
language eng
dc.rights.none.fl_str_mv open access
http://purl.org/coar/access_right/c_abf2
https://rightsstatements.org/vocab/InC/1.0/
dc.rights.openaire.fl_str_mv info:eu-repo/semantics/openAccess
rights_invalid_str_mv open access
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eu_rights_str_mv openAccess
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dc.source.none.fl_str_mv reponame:Dipòsit Digital de Documents de la UAB
instname:Universitat Autònoma de Barcelona
instname_str Universitat Autònoma de Barcelona
reponame_str Dipòsit Digital de Documents de la UAB
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