Data mining applied to the cognitive rehabilitation of patients with acquired brain injury

Marcano Cedeño, Alexis Enrique; Chausa Fernández, Paloma; García, Alejandro; Cáceres Taladriz, César; Tormos Muñoz, Josep M. y Gómez Aguilera, Enrique J. (2013). Data mining applied to the cognitive rehabilitation of patients with acquired brain injury. "Expert Systems with Applications", v. 40 (n. 4); pp. 1054-1060. ISSN 0957-4174. https://doi.org/10.1016/j.eswa.2012.08.034.

Descripción

Título: Data mining applied to the cognitive rehabilitation of patients with acquired brain injury
Autor/es:
  • Marcano Cedeño, Alexis Enrique
  • Chausa Fernández, Paloma
  • García, Alejandro
  • Cáceres Taladriz, César
  • Tormos Muñoz, Josep M.
  • Gómez Aguilera, Enrique J.
Tipo de Documento: Artículo
Título de Revista/Publicación: Expert Systems with Applications
Fecha: Marzo 2013
Volumen: 40
Materias:
Palabras Clave Informales: Acquired brain injury; Cognitive rehabilitation; Data mining; Decision tree; Multilayer perceptron; General regression neural network
Escuela: E.T.S.I. Telecomunicación (UPM)
Departamento: Tecnología Fotónica [hasta 2014]
Licencias Creative Commons: Reconocimiento - Sin obra derivada - No comercial

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Resumen

Acquired brain injury (ABI) is one of the leading causes of death and disability in the world and is associated with high health care costs as a result of the acute treatment and long term rehabilitation involved. Different algorithms and methods have been proposed to predict the effectiveness of rehabilitation programs. In general, research has focused on predicting the overall improvement of patients with ABI. The purpose of this study is the novel application of data mining (DM) techniques to predict the outcomes of cognitive rehabilitation in patients with ABI. We generate three predictive models that allow us to obtain new knowledge to evaluate and improve the effectiveness of the cognitive rehabilitation process. Decision tree (DT), multilayer perceptron (MLP) and general regression neural network (GRNN) have been used to construct the prediction models. 10-fold cross validation was carried out in order to test the algorithms, using the Institut Guttmann Neurorehabilitation Hospital (IG) patients database. Performance of the models was tested through specificity, sensitivity and accuracy analysis and confusion matrix analysis. The experimental results obtained by DT are clearly superior with a prediction average accuracy of 90.38%, while MLP and GRRN obtained a 78.7% and 75.96%, respectively. This study allows to increase the knowledge about the contributing factors of an ABI patient recovery and to estimate treatment efficacy in individual patients.

Más información

ID de Registro: 16285
Identificador DC: http://oa.upm.es/16285/
Identificador OAI: oai:oa.upm.es:16285
Identificador DOI: 10.1016/j.eswa.2012.08.034
URL Oficial: http://www.sciencedirect.com/science/article/pii/S0957417412009955
Depositado por: Memoria Investigacion
Depositado el: 10 Jul 2013 18:23
Ultima Modificación: 01 Abr 2015 22:56
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