License: Creative Commons Attribution 3.0 Unported license (CC BY 3.0)
When quoting this document, please refer to the following
DOI: 10.4230/OASIcs.ICCSW.2017.4
URN: urn:nbn:de:0030-drops-84462
URL: https://drops.dagstuhl.de/opus/volltexte/2018/8446/
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Kurz, Christoph F. ; Holle, Rolf

Demand for Medical Care by the Elderly: A Nonparametric Variational Bayesian Mixture Approach

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OASIcs-ICCSW-2017-4.pdf (0.4 MB)


Abstract

Outpatient care is a large share of total health care spending, making analysis of data on outpatient utilization an important part of understanding patterns and drivers of health care spending growth. Common features of outpatient utilization measures include zero-inflation, over-dispersion, and skewness, all of which complicate statistical modeling. Mixture modeling is a popular approach because it can accommodate these features of health care utilization data. In this work, we add a nonparametric clustering component to such models. Our fully Bayesian model framework allows for an unknown number of mixing components, so that the data, rather than the researcher, determine the number of mixture components. We apply the modeling framework to data on visits to physicians by elderly individuals and show that each subgroup has different characteristics that allow easy interpretation and new insights.

BibTeX - Entry

@InProceedings{kurz_et_al:OASIcs:2018:8446,
  author =	{Christoph F. Kurz and Rolf Holle},
  title =	{{Demand for Medical Care by the Elderly: A Nonparametric Variational Bayesian Mixture	Approach}},
  booktitle =	{2017 Imperial College Computing Student Workshop (ICCSW 2017)},
  pages =	{4:1--4:7},
  series =	{OpenAccess Series in Informatics (OASIcs)},
  ISBN =	{978-3-95977-059-0},
  ISSN =	{2190-6807},
  year =	{2018},
  volume =	{60},
  editor =	{Fergus Leahy and Juliana Franco},
  publisher =	{Schloss Dagstuhl--Leibniz-Zentrum fuer Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{http://drops.dagstuhl.de/opus/volltexte/2018/8446},
  URN =		{urn:nbn:de:0030-drops-84462},
  doi =		{10.4230/OASIcs.ICCSW.2017.4},
  annote =	{Keywords: machine learning, health care utilization, Bayesian statistics}
}

Keywords: machine learning, health care utilization, Bayesian statistics
Collection: 2017 Imperial College Computing Student Workshop (ICCSW 2017)
Issue Date: 2018
Date of publication: 21.02.2018


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