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Towards Hospitalization After Readmission Risk Prediction Using ELMs

  • Jose Manuel Lopez-GuedeEmail author
  • Asier Garmendia
  • Manuel Graña
  • Sebastian Rios
  • Julian Estevez
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10338)

Abstract

A criteria to evaluate the performance of Emergency Departments (ED) is the number of readmissions and hospitalizations short time after discharge of patients because the problem was not solved in the first admission. Such events contribute to overload the care system and to worsening the health of patients. In this paper we address the problem of predicting hospitalization events after readmission in ED, facing it as a classification problem and using Extreme Learning Machines (ELM). We have carried out experiments with a dataset with 45,089 admission events of 21,269 pediatric patients recorded in the Hospital José Joaquín Aguirre of the University of Chile during 3 years and 4 months, improving the state-of-the-art sensitivity results on the same dataset by 17%.

Keywords

Emergency Department Extreme Learn Machine Minority Class Fragility Index Hospitalization Event 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Notes

Acknowledgments

The research was supported by the Computational Intelligence Group of the Basque Country University (UPV/EHU) through Grant IT874-13 of Research Groups Call 2013–2017 (Basque Country Government).

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Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • Jose Manuel Lopez-Guede
    • 1
    Email author
  • Asier Garmendia
    • 1
  • Manuel Graña
    • 1
  • Sebastian Rios
    • 2
  • Julian Estevez
    • 1
  1. 1.Computational Intelligence GroupBasque Country University (UPV/EHU)San SebastianSpain
  2. 2.Business Intelligence Research Center (CEINE), Industrial Engineering DepartmentUniversity of ChileSantiagoChile

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