HealthSCOPE: An Interactive Distributed Data Mining Framework for Scalable Prediction of Healthcare Costs

In this demonstration proposal we describe Health- SCOPE (Healthcare Scalable COst Prediction Engine), a framework for exploring historical and present day healthcare costs as well as for predicting future costs. HealthSCOPE can be used by individuals to estimate their healthcare costs in the coming year. In addition, HealthSCOPE supports a population based view for actuaries and insurers who want to estimate the future costs of a population based on historical claims data, a typical scenario for accountable care organizations (ACOs). Using our interactive data mining framework, users can view claims (sample files will be provided), use HealthSCOPE to predict costs for the upcoming year, interactively select from a set of possible medical conditions, understand the factors that contribute to the cost, and compare costs against historical averages. The back-end system contains cloud based prediction services hosted on the Microsoft Azure infrastructure that allow the easy deployment of models encoded in Predictive Model Markup Language (PMML) and trained using either Spark MLLib or various non-distributed environments.
James Marquardt∗, Stacey Newman∗, Deepa Hattarki∗, Rajagopalan Srinivasan∗, Shanu Sushmita∗, Prabhu Ram†, Viren Prasad†, David Hazel∗, Archana Ramesh∗, Martine De Cock∗‡, Ankur Teredesai∗
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