2 Scopus citations

Abstract

Healthcare data from the Arizona Health Care Cost Containment System, Arizona's Medicaid program provides a unique opportunity to exploit state-of-the-art data processing and analysis algorithms to mine data and provide actionable findings that can aid cost containment. Our work addresses specific challenges in this real-life healthcare application to build predictive risk models for forecasting future high-cost patients. We survey the literature and propose novel data mining approaches customized for this compelling application with specific focus on non-random sampling. Our empirical study indicates that the proposed approach is highly effective and can benefit further research on cost containment in the healthcare industry.

Original languageEnglish (US)
Title of host publicationHEALTHINF 2008 - 1st International Conference on Health Informatics, Proceedings
Pages126-133
Number of pages8
StatePublished - Dec 15 2008
Event1st International Conference on Health Informatics, HEALTHINF 2008 - Funchal, Madeira, Portugal
Duration: Jan 28 2008Jan 31 2008

Publication series

NameHEALTHINF 2008 - 1st International Conference on Health Informatics, Proceedings
Volume1

Other

Other1st International Conference on Health Informatics, HEALTHINF 2008
CountryPortugal
CityFunchal, Madeira
Period1/28/081/31/08

Keywords

  • Classification
  • Data mining
  • Healthcare costs
  • High-cost patients
  • High-risk patients
  • Imbalanced data
  • Medicaid
  • Non-random sampling
  • Over-sampling
  • Predictive risk modeling
  • Skewed data
  • Under-sampling

ASJC Scopus subject areas

  • Biomedical Engineering
  • Health Informatics
  • Health Information Management

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