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 frame-work for exploring historical and present day healthcare costs as well as for predicting future costs. Health SCOPE can be used by individuals to estimate their healthcare costs in the comin...

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Bibliographic Details
Published in:	2014 IEEE International Conference on Data Mining Workshop pp. 1227 - 1230
Main Authors:	Marquardt, Ames, Newman, Stacey, Hattarki, Deepa, Srinivasan, Rajagopalan, Sushmita, Shanu, Ram, Prabhu, Prasad, Viren, Hazel, David, Ramesh, Archana, De Cock, Martine, Teredesai, Ankur
Format:	Conference Proceeding
Language:	English
Published:	IEEE 01-12-2014
Subjects:	Computational modeling Data mining Data models distributed data mining Healthcare cost prediction insurance claims data Medical services Microsoft Azure PMML Predictive models Sociology Spark Statistics
Online Access:	Get full text
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Summary:	In this demonstration proposal we describe Health-SCOPE (Healthcare Scalable COst Prediction Engine), a frame-work for exploring historical and present day healthcare costs as well as for predicting future costs. Health SCOPE can be used by individuals to estimate their healthcare costs in the coming year. In addition, Health SCOPE 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 Health SCOPE 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.
ISSN:	2375-9232 2375-9259
DOI:	10.1109/ICDMW.2014.45