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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. The work addresses specific challenges in this real-life healthcare application to build predictive risk models for forecasting future high-cost patients. The authors survey the literature and propose novel data mining approaches customized for this compelling application with specific focus on non-random sampling.
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