A Hierarchical Ensemble of α-Trees for Predicting Expensive Hospital Visits
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- 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
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.
Keywordsdecision tree α-divergence ensemble classifiers healthcare
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