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Comparative Effectiveness Of Total Population Versus Disease-Specific Neural Network Models In Predicting Medical Costs, Albert G. Crawford, Joseph P. Fuhr Jr., Janice Clarke, Brandon Hubbs

College of Population Health Faculty Papers

The objective of this research was to compare the accuracy of two types of neural networks in identifying individuals at risk for high medical costs for three chronic conditions. Two neural network models—a population model and three disease-specific models—were compared regarding effectiveness predicting high costs. Subjects included 33,908 health plan members with diabetes, 19,264 with asthma, and 2,605 with cardiac conditions. For model development/testing, only members with 24 months of continuous enrollment were included. Models were developed to predict probability of high costs in 2000 (top 15% of distribution) based on 1999 claims factors. After validation, models were applied to …