Predicting EQ-5D from the Parkinson's disease questionnaire PDQ-8 using multi-dimensional Bayesian network classifiers

Borchani, Hanen; Bielza Lozoya, Maria Concepcion; Martínez-Martín, Pablo y Larrañaga Múgica, Pedro (2014). Predicting EQ-5D from the Parkinson's disease questionnaire PDQ-8 using multi-dimensional Bayesian network classifiers. "Biomedical Engineering: Applications, Basis And Communications", v. 26 (n. 1); pp. 1-11. ISSN 1016-2372. https://doi.org/10.4015/S101623721450015X.

Descripción

Título: Predicting EQ-5D from the Parkinson's disease questionnaire PDQ-8 using multi-dimensional Bayesian network classifiers
Autor/es:
  • Borchani, Hanen
  • Bielza Lozoya, Maria Concepcion
  • Martínez-Martín, Pablo
  • Larrañaga Múgica, Pedro
Tipo de Documento: Artículo
Título de Revista/Publicación: Biomedical Engineering: Applications, Basis And Communications
Fecha: Febrero 2014
Volumen: 26
Materias:
Palabras Clave Informales: Parkinson's disease; EQ-5D; PDQ-8; Health-related quality of life; Bayesian networks
Escuela: E.T.S. de Ingenieros Informáticos (UPM)
Departamento: Inteligencia Artificial
Licencias Creative Commons: Reconocimiento - Sin obra derivada - No comercial

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Resumen

The impact of the Parkinson's disease and its treatment on the patients' health-related quality of life can be estimated either by means of generic measures such as the european quality of Life-5 Dimensions (EQ-5D) or specific measures such as the 8-item Parkinson's disease questionnaire (PDQ-8). In clinical studies, PDQ-8 could be used in detriment of EQ-5D due to the lack of resources, time or clinical interest in generic measures. Nevertheless, PDQ-8 cannot be applied in cost-effectiveness analyses which require generic measures and quantitative utility scores, such as EQ-5D. To deal with this problem, a commonly used solution is the prediction of EQ-5D from PDQ-8. In this paper, we propose a new probabilistic method to predict EQ-5D from PDQ-8 using multi-dimensional Bayesian network classifiers. Our approach is evaluated using five-fold cross-validation experiments carried out on a Parkinson's data set containing 488 patients, and is compared with two additional Bayesian network-based approaches, two commonly used mapping methods namely, ordinary least squares and censored least absolute deviations, and a deterministic model. Experimental results are promising in terms of predictive performance as well as the identification of dependence relationships among EQ-5D and PDQ-8 items that the mapping approaches are unable to detect

Más información

ID de Registro: 35611
Identificador DC: http://oa.upm.es/35611/
Identificador OAI: oai:oa.upm.es:35611
Identificador DOI: 10.4015/S101623721450015X
URL Oficial: http://www.worldscientific.com/doi/abs/10.4015/S101623721450015X
Depositado por: Memoria Investigacion
Depositado el: 14 Jul 2015 10:19
Ultima Modificación: 17 Nov 2017 09:02
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