Identificación de los determinantes de la estadía en Unidades de Cuidados Intensivos usando redes neuronales artificiales

The prediction of the length of stay at the moment of hospital admission is of outmost importance. Many studies have used lineal models to predict this variable, but there are inherent limitations to these models. The use of non lineal models has been scarce. Aim: To develop a non lineal model to pr...

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Autores principales:	Chacón P,Max, Rocco M,Víctor, Morgado A,Enrique, Sáez H,Enzo, Pliscoff M,Sergio
Lenguaje:	Spanish / Castilian
Publicado:	Sociedad Médica de Santiago 2002
Materias:	Intensive care units Models, theoretical, Neural networks (computer)
Acceso en línea:	http://www.scielo.cl/scielo.php?script=sci_arttext&pid=S0034-98872002000100010
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spelling	oai:scielo:S0034-988720020001000102002-04-09Identificación de los determinantes de la estadía en Unidades de Cuidados Intensivos usando redes neuronales artificialesChacón P,MaxRocco M,VíctorMorgado A,EnriqueSáez H,EnzoPliscoff M,Sergio Intensive care units Models, theoretical, Neural networks (computer) The prediction of the length of stay at the moment of hospital admission is of outmost importance. Many studies have used lineal models to predict this variable, but there are inherent limitations to these models. The use of non lineal models has been scarce. Aim: To develop a non lineal model to predict length of stay in intensive care units. Material and methods: Retrospective analysis of 294 patients admitted to two intensive care units in Santiago, Chile. The severity of the disease motivating the admission was nominally quantified. This and other physiological variables were included in the model. The length of stay was modeled using Artificial Neural Networks. Results: The model yielded an error of 8.7% (3.6 ± 0.4 days, CI 95%) and a correlation coefficient of 0.9 (p <0.001) for the prediction of length of stay. Using net sensitivity analysis, the model determined that gastrointestinal diseases, infections and respiratory problems were the main causes of prolongation of intensive care unit stay. Conclusions: Intensive care units should have, in the future, computer systems that gather vital information to predict length of stay (Rev Méd Chile 2002; 130: 71-78)info:eu-repo/semantics/openAccessSociedad Médica de SantiagoRevista médica de Chile v.130 n.1 20022002-01-01text/htmlhttp://www.scielo.cl/scielo.php?script=sci_arttext&pid=S0034-98872002000100010es10.4067/S0034-98872002000100010
institution	Scielo Chile
collection	Scielo Chile
language	Spanish / Castilian
topic	Intensive care units Models, theoretical, Neural networks (computer)
spellingShingle	Intensive care units Models, theoretical, Neural networks (computer) Chacón P,Max Rocco M,Víctor Morgado A,Enrique Sáez H,Enzo Pliscoff M,Sergio Identificación de los determinantes de la estadía en Unidades de Cuidados Intensivos usando redes neuronales artificiales
description	The prediction of the length of stay at the moment of hospital admission is of outmost importance. Many studies have used lineal models to predict this variable, but there are inherent limitations to these models. The use of non lineal models has been scarce. Aim: To develop a non lineal model to predict length of stay in intensive care units. Material and methods: Retrospective analysis of 294 patients admitted to two intensive care units in Santiago, Chile. The severity of the disease motivating the admission was nominally quantified. This and other physiological variables were included in the model. The length of stay was modeled using Artificial Neural Networks. Results: The model yielded an error of 8.7% (3.6 ± 0.4 days, CI 95%) and a correlation coefficient of 0.9 (p <0.001) for the prediction of length of stay. Using net sensitivity analysis, the model determined that gastrointestinal diseases, infections and respiratory problems were the main causes of prolongation of intensive care unit stay. Conclusions: Intensive care units should have, in the future, computer systems that gather vital information to predict length of stay (Rev Méd Chile 2002; 130: 71-78)
author	Chacón P,Max Rocco M,Víctor Morgado A,Enrique Sáez H,Enzo Pliscoff M,Sergio
author_facet	Chacón P,Max Rocco M,Víctor Morgado A,Enrique Sáez H,Enzo Pliscoff M,Sergio
author_sort	Chacón P,Max
title	Identificación de los determinantes de la estadía en Unidades de Cuidados Intensivos usando redes neuronales artificiales
title_short	Identificación de los determinantes de la estadía en Unidades de Cuidados Intensivos usando redes neuronales artificiales
title_full	Identificación de los determinantes de la estadía en Unidades de Cuidados Intensivos usando redes neuronales artificiales
title_fullStr	Identificación de los determinantes de la estadía en Unidades de Cuidados Intensivos usando redes neuronales artificiales
title_full_unstemmed	Identificación de los determinantes de la estadía en Unidades de Cuidados Intensivos usando redes neuronales artificiales
title_sort	identificación de los determinantes de la estadía en unidades de cuidados intensivos usando redes neuronales artificiales
publisher	Sociedad Médica de Santiago
publishDate	2002
url	http://www.scielo.cl/scielo.php?script=sci_arttext&pid=S0034-98872002000100010
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Identificación de los determinantes de la estadía en Unidades de Cuidados Intensivos usando redes neuronales artificiales

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