Computational intelligence contributions to readmisision risk prediction in healthcare systems

Supervised by:
  1. Andoni Beristain Iraola Director
  2. Manuel Graña Romay Director

Defence university: Universidad del País Vasco - Euskal Herriko Unibertsitatea

Fecha de defensa: 26 October 2017

  1. María Camino Rodríguez Vela Chair
  2. Ana Isabel González Acuña Secretary
  3. José Ramiro Varela Arias Committee member
  4. Sebastian Rios Perez Committee member
  5. José Manuel López Guede Committee member
  1. Ciencia de la Computación e Inteligencia Artificial

Type: Thesis

Teseo: 143893 DIALNET lock_openADDI editor


The Thesis tackles the problem of readmission risk prediction in healthcare systems from a machine learning and computational intelligence point of view. Readmission has been recognized as an indicator of healthcare quality with primary economic importance. We examine two specific instances of the problem, the emergency department (ED) admission and heart failure (HF) patient care using anonymized datasets from three institutions to carry real-life computational experiments validating the proposed approaches. The main difficulties posed by this kind of datasets is their high class imbalance ratio, and the lack of informative value of the recorded variables. This thesis reports the results of innovative class balancing approaches and new classification architectures.