A Hierarchical Ensemble of α-Trees for Predicting Expensive Hospital Visits

  • Yubin Park
  • Joydeep Ghosh
Conference paper

DOI: 10.1007/978-3-319-09891-3_17

Part of the Lecture Notes in Computer Science book series (LNCS, volume 8609)
Cite this paper as:
Park Y., Ghosh J. (2014) A Hierarchical Ensemble of α-Trees for Predicting Expensive Hospital Visits. In: Ślȩzak D., Tan AH., Peters J.F., Schwabe L. (eds) Brain Informatics and Health. BIH 2014. Lecture Notes in Computer Science, vol 8609. Springer, Cham

Abstract

Hospital charges are determined by numerous factors. Even the cost for the same procedure can vary greatly depending on a patient’s conditions, complications, and types of facilities. With the advent of Obamacare, estimating hospital charges has become an increasingly important problem in healthcare informatics. We propose a hierarchical ensemble of α-Trees to delicately deal with this challenging problem. In the proposed approach, multiple α-Trees are built to capture the different aspects of hospital charges, and then these multiple classifiers are uniquely combined for each hospital. Hospitals are characterized by unique weight vectors that explain the subtle differences in hospital specialties and patient groups. Experimental results based on the 2006 Texas inpatient discharge data show that our approach effectively captures the variability of hospital charges across different hospitals, and also provides a useful characterization of different hospitals in the process.

Keywords

decision tree α-divergence ensemble classifiers healthcare 

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Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Yubin Park
    • 1
  • Joydeep Ghosh
    • 1
  1. 1.The University of Texas at AustinAustinUSA

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