Machine Learning Readmission Risk Modeling: A Pediatric Case Study.

Journal: BioMed research international
Published Date: Apr 15, 2019

Abstract

BACKGROUND: Hospital readmission prediction in pediatric hospitals has received little attention. Studies have focused on the readmission frequency analysis stratified by disease and demographic/geographic characteristics but there are no predictive modeling approaches, which may be useful to identify preventable readmissions that constitute a major portion of the cost attributed to readmissions.

Authors

  • Patricio Wolff
    Research Center on Business Intelligence, University of Chile, Beauchef 851, Of. 502, Santiago, Chile.
  • Manuel Graña
    Computational Intelligence Group, Faculty of Informatics, Basque Country University (UPV/EHU), Paseo Manuel de Lardizabal 1, 20018 San Sebastian, Spain; Department of Computer Science and Artificial Intelligence, Faculty of Informatics, Basque Country University (UPV/EHU), Paseo Manuel de Lardizabal 1, 20018 San Sebastian, Spain; ENGINE Centre, Wrocław University of Technology, Wybrzeże Wyspiańskiego 27, 50-370 Wrocław, Poland.
  • Sebastián A Ríos
    Research Center on Business Intelligence, University of Chile, Beauchef 851, Of. 502, Santiago, Chile.
  • Maria Begoña Yarza
    Hospital Dr. Exequiel González Cortés, Gran Avenida 3300, San Miguel, Santiago, Chile.

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