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

Marcano Cedeño, Alexis Enrique and Chausa Fernández, Paloma and García, Alejandro and Cáceres Taladriz, César and Tormos Muñoz, Josep M. and 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.

Description

Title: Data mining applied to the cognitive rehabilitation of patients with acquired brain injury
Author/s:
  • Marcano Cedeño, Alexis Enrique
  • Chausa Fernández, Paloma
  • García, Alejandro
  • Cáceres Taladriz, César
  • Tormos Muñoz, Josep M.
  • Gómez Aguilera, Enrique J.
Item Type: Article
Título de Revista/Publicación: Expert Systems with Applications
Date: March 2013
ISSN: 0957-4174
Volume: 40
Subjects:
Freetext Keywords: Acquired brain injury; Cognitive rehabilitation; Data mining; Decision tree; Multilayer perceptron; General regression neural network
Faculty: E.T.S.I. Telecomunicación (UPM)
Department: Tecnología Fotónica [hasta 2014]
Creative Commons Licenses: Recognition - No derivative works - Non commercial

Full text

[img]
Preview
PDF - Requires a PDF viewer, such as GSview, Xpdf or Adobe Acrobat Reader
Download (3MB) | Preview

Abstract

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.

More information

Item ID: 16285
DC Identifier: http://oa.upm.es/16285/
OAI Identifier: oai:oa.upm.es:16285
DOI: 10.1016/j.eswa.2012.08.034
Official URL: http://www.sciencedirect.com/science/article/pii/S0957417412009955
Deposited by: Memoria Investigacion
Deposited on: 10 Jul 2013 18:23
Last Modified: 01 Apr 2015 22:56
  • Logo InvestigaM (UPM)
  • Logo GEOUP4
  • Logo Open Access
  • Open Access
  • Logo Sherpa/Romeo
    Check whether the anglo-saxon journal in which you have published an article allows you to also publish it under open access.
  • Logo Dulcinea
    Check whether the spanish journal in which you have published an article allows you to also publish it under open access.
  • Logo de Recolecta
  • Logo del Observatorio I+D+i UPM
  • Logo de OpenCourseWare UPM