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Comparative Effectiveness for Oral Anti-diabetic Treatments among Newly Diagnosed Type-2 Diabetics: Machine Learning Applied to a Large-Scale Claims Dataset

Authors: Maguire, Jon
Dhar, Vasant
Keywords: diabetes, comparative effectiveness, healthcare, informatics, data mining, machine learning, claims data, health insurance
Issue Date: 7-Nov-2011
Series/Report no.: CeDER-11-07
Abstract: In this paper, we demonstrate how the US healthcare system can provide increased benefits per unit of spend, and earlier identification of and intervention in chronic diseases through better predictive data-based analytics applied to the increasingly available troves of healthcare claims data. Specifically, we demonstrate the effectiveness of data mining by applying machine learning methods to large-scale medical and pharmacy claims data for roughly 70,000 patients over six years on newly diagnosed with type-2 diabetes, a common disease in the US costing billions to treat. This analysis reveals important differences in cost and quality among the disease's common treatments some of which have been published in the American Diabetes Association, and others that are regarded as tentative or have not been considered at all. The study demonstrates the potential for using large scale data mining for better understanding other major diseases including coronary problems and cancers and for focusing further inquiry in these areas.
Appears in Collections:CeDER Working Papers

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