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Emergency Department Readmission Risk Prediction: A Case Study in Chile

  • Arkaitz Artetxe
  • Manuel GrañaEmail author
  • Andoni Beristain
  • Sebastián Ríos
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10338)

Abstract

Short time readmission prediction in Emergency Departments (ED) is a valuable tool to improve both the ED management and the healthcare quality. It helps identifying patients requiring further post-discharge attention as well as reducing healthcare costs. As in many other medical domains, patient readmission data is heavily imbalanced, i.e. the minority class is very infrequent, which is a challenge for the construction of accurate predictors using machine learning tools. We have carried computational experiments on a dataset composed of ED admission records spanning more than 100000 patients in 3 years, with a highly imbalanced distribution. We employed various approaches for dealing with this highly imbalanced dataset in combination with different classification algorithms and compared their predictive power for the estimation of the ED readmission probability within 72 h after discharge. Results show that random undersampling and Bagging (RUSBagging) in combination with Random Forest achieves the best results in terms of Area Under ROC Curve (AUC).

Keywords

Readmission risk Imbalanced data Classification Bagging 

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Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • Arkaitz Artetxe
    • 1
    • 2
  • Manuel Graña
    • 2
    Email author
  • Andoni Beristain
    • 1
  • Sebastián Ríos
    • 3
  1. 1.Vicomtech-IK4 Research CentreSan SebastianSpain
  2. 2.Computation Intelligence GroupBasque University (UPV/EHU)San SebastianSpain
  3. 3.Business Intelligence Research Center (CEINE), Industrial Engineering DepartmentUniversity of ChileSantiagoChile

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