Healthcare Risk Adjustment and Predictive Modeling provides a �comprehensive guide to healthcare actuaries and other �professionals �interested in �healthcare data analytics, risk adjustment and �predictive �modeling. The book first �introduces the topic with discussions of health risk, available data, �clinical �identification �algorithms for diagnostic �grouping and the use of grouper �models. The �second part of the book �presents the �concept of data mining and some of the �common approaches used by modelers. The third and �final section �covers a �number of �predictive �modeling and risk �adjustment �case-studies, with �examples from �Medicaid, Medicare, Disability, �depression diagnosis and �provider �reimbursement, as well as the use of predictive modeling and risk �adjustment outside the U.S. For readers who wish to experiment with their own models, the book also provides a test dataset.

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