Survival prediction in patients undergoing ischemic cardiopathy

  • Authors:

  • E. Soria;J. D. Martín;J. Caravaca;A. J. Serrano;M. Martínez;R. Magdalena;J. Gómez;M. Heras;G. Sanz

  • Affiliations:

  • Intelligent Data Analysis Laboratory, Department of Electronic Engineering, University of Valencia, Valencia, Spain;Intelligent Data Analysis Laboratory, Department of Electronic Engineering, University of Valencia, Valencia, Spain;Intelligent Data Analysis Laboratory, Department of Electronic Engineering, University of Valencia, Valencia, Spain;Intelligent Data Analysis Laboratory, Department of Electronic Engineering, University of Valencia, Valencia, Spain;Intelligent Data Analysis Laboratory, Department of Electronic Engineering, University of Valencia, Valencia, Spain;Intelligent Data Analysis Laboratory, Department of Electronic Engineering, University of Valencia, Valencia, Spain;Intelligent Data Analysis Laboratory, Department of Electronic Engineering, University of Valencia, Valencia, Spain;Servicio de Cardiología, Instituto Clínico del Tórax e Institut d'Investigacions Biomédiques Agustí Pi i Sunyer, Hospital Clínic de Barcelona;Departamento de Investigación Cardiovascular Tralacional de Nuevas Tecnologías y Terapias, Fundación Centro Nacional de Investigaciones Cardiovasculares Carlos III

  • Venue:
  • IJCNN'09 Proceedings of the 2009 international joint conference on Neural Networks
  • Year:
  • 2009

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Abstract

The ischemic cardiopathy is the main cause of death in developed countries. New improved drugs and therapies have appeared last years. However, the interventionist strategy and the most powerful drugs may have complications, and hence, it is very important to know the risk of death associated with patients during their stay in the hospital, or In the next six months. Thus, it is possible to tune the best treatment for each individual patient. In this framework, the use of artificial neural networks is proposed with a double objective: survival prediction and the extraction of the parameters with best predictive capabilities. A cohort of 691 patients treated in the Hospital Clínic, in Barcelona (Spain) during the period 2006-08 was used for this study. The obtained results show the good prediction capabilities of neural models when compared with classical models (logistic regression) and decision trees. Moreover, neural models reduced the number of relevant variables for the prediction from 134 to only 36.