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.
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References
Amari, S.: Integration of stochastic models by minimizing α-divergence. Neural Computation (2007)
Block, M., Cox, A., McGinty, J.C., Ericson, M.: How much hospitals charge for the same procedures (May 2013), http://www.nytimes.com/interactive/2013/05/08/business/how-much-hospitals-charge.html?ref=business&version=meter+at+8®ion=FixedCenter&pgtype=Multimedia&priority=true&module=RegiWall-Regi&action=click
Breiman, L.: Classification and regression trees. Wadsworth International Group (1984)
Breiman, L.: Random forests. Journal of Machine Learning 45(1), 5–32 (2001)
Centers for Medicare and Medicaid Services: Medicare provider charge data. online (January 2014), http://www.cms.gov/Research-Statistics-Data-and-Systems/Statistics-Trends-and-Reports/Medicare-Provider-Charge-Data/index.html
Chernoff, H.: A measure of asymptotic efficiency for tests of a hypothesis based on the sum of observations. The Annals of Mathematical Statistics (1952)
Cichocki, A., Ishi Amari, S.: Families of alpha- beta- and gamma- divergences: Flexible and robust measures of similarities. Entropy (2010)
Cipriano, L.E., Steinberg, M.L., Gazelle, G.S., Gonzalez, R.G.: Comparing and predicting the costs and outcomes of patients with major and minor stroke using the boston acute stroke imaging scale neuroimaging classification system. American Journal of Neuroradiology 30, 703–709 (2009)
Csiszar, I.: Information-type measures of difference of probability distributions and indirect observation. Studia Scientiarum Mathematicarum Hungarica (1967)
Dietterich, T., Kearns, M., Mansour, Y.: Applying the weak learning framework to understand and improve c4.5. In: Proceedings of the Thirteenth International Conference on Machine Learning, pp. 96–104 (1996)
Diringer, M.N., Edwards, D.F., Mattson, T., Akins, P.T., Sheedy, C.W., Hsu, C.Y., Dromerick, A.W.: Predictors of acute hospital costs for treatment of ischemic stroke in an academic center. Stroke 30, 724–728 (1999)
Duda, R.O., Hart, P.E., Stork, D.G.: Pattern Classification. Wiley (2001)
Gawande, A.: The cost conundrum (June 2009), http://www.newyorker.com/reporting/2009/06/01/090601fa_fact_gawande?currentPage=1
Gelman, A., Hill, J.: Data Analysis using Regression and Multilevel/Hierarchical Models. Cambridge University Press (2007)
Goldstein, H.: Multilevel Statistical Models, 4th edn. Wiley (2010)
Jost, L.: Entropy and diversity. Oikos (2006)
Meier, B., McGinty, J.C., Creswell, J.: Hospital billing varies wildly, government data shows (May 2013), http://www.nytimes.com/2013/05/08/business/hospital-billing-varies-wildly-us-data-shows.html
Park, Y., Ghosh, J.: Compact ensemble trees for imbalanced data. In: Sansone, C., Kittler, J., Roli, F. (eds.) MCS 2011. LNCS, vol. 6713, pp. 86–95. Springer, Heidelberg (2011)
Park, Y., Ghosh, J.: Ensembles of α-Trees for Imbalanced Classification Problems. IEEE Transactions on Knowledge and Data Engineering 26(1), 131–143 (2014)
Quinlan, J.R.: C4.5: prgrams for machine learning. Morgan kaufmann (1993)
Renyi, A.: On measures of information and entropy. In: Proceedings of the Fourth Berkeley Symposium on Mathematics (1961)
Tsallis, C.: Possible generalization of boltzmann-gibbs statistics. Journal of Statistical Physics (1988)
Wang, J., Li, M., Hu, Y., Zhu, Y.: Comparison of hospital charge prediction models for gastric cancer patients: neural network vs. decision tree models. BMC Health Services Research 9 (2009)
Zhu, H., Rohwer, R.: Information geometric measurements of generalization. Tech. Rep. 4350, Aston University (1995)
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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. https://doi.org/10.1007/978-3-319-09891-3_17
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DOI: https://doi.org/10.1007/978-3-319-09891-3_17
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-09890-6
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