Tesis doctorales: TESEO

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Ficha de Tesis

Título: COMPUTATIONAL INTELLIGENCE CONTRIBUTIONS TO READMISISION RISK PREDICTION IN HEALTHCARE SYSTEMS
Autor: ARTETXE BALLEJO, ARKAITZ
Universidad: Universidad del País Vasco/Euskal Herriko Unibertsitatea
Departamento: Ingeniería de sistemas y automática
Fecha de Lectura: 26/10/2017
Programa de doctorado: Programa de Doctorado en Ingeniería Informática por la Universidad del País Vasco/Euskal Herriko Unibertsitatea
Dirección:
- BERISTAIN IRAOLA, ANDONI (Director)
- GRAÑA ROMAY, MANUEL (Director)
Tribunal:
- RODRÍGUEZ VELA, MARÍA CAMINO (presidente)
- GONZÁLEZ ACUÑA, ANA ISABEL (secretario)
- VARELA ARIAS, JOSÉ RAMIRO (vocal)
- RIOS PEREZ, SEBASTIAN (vocal)
- LÓPEZ GUEDE, JOSÉ MANUEL (vocal)
Descriptores:
- INFORMATICA
Localización: REPOSITORIO INSTITUCIONAL E.H.U./U.P.V.

Marcador: https://www.educacion.gob.es/teseo/mostrarRef.do?ref=431679Añadir a favoritos.
Resumen:
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.

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