Authors:
Sai T. Moturu
;
Huan Liu
and
William G. Johnson
Affiliation:
Arizona State University, United States
Keyword(s):
Predictive risk modeling, healthcare costs, high-cost patients, high-risk patients, non-random sampling, over-sampling, under-sampling, imbalanced data, skewed data, Medicaid, data mining, classification.
Related
Ontology
Subjects/Areas/Topics:
Artificial Intelligence
;
Biomedical Engineering
;
Business Analytics
;
Cardiovascular Technologies
;
Computing and Telecommunications in Cardiology
;
Data Engineering
;
Data Mining
;
Databases and Information Systems Integration
;
Datamining
;
Enterprise Information Systems
;
Health Engineering and Technology Applications
;
Health Information Systems
;
Medical and Nursing Informatics
;
Sensor Networks
;
Signal Processing
;
Soft Computing
Abstract:
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. Our work addresses specific challenges in this real-life healthcare application to build predictive risk models for forecasting future high-cost patients. We survey the literature and propose novel data mining approaches customized for this compelling application with specific focus on
non-random sampling. Our empirical study indicates that the proposed approach is highly effective and can benefit further research on cost containment in the healthcare industry.