Abstract: A computerized system and method for estimating levels of obesity in an insured population using claims data. The model uses health risk assessment data comprising age, height, and weight information as well as information about health conditions and health behaviors for a member population. Claims data is used to train a two-stage model on the member population. The first stage comprises a support vector machine, a rule-based module, and a generalized linear model that estimates the probability of obesity. The second stage comprises a regression neural network that operates on the output of the first stage and a subset of the input feature vector. Cost and utilizations in these areas, along with overall health measures as well as demographics and social factors, are inputs to a set of pattern recognition engines that perform regression. The output is the estimated body mass index of the member.

Abstract: A computerized system and method for estimating levels of obesity in an insured population using claims data. The model uses health risk assessment data comprising age, height, and weight information as well as information about health conditions and health behaviors for a member population. Claims data is used to train a two-stage model on the member population. The first stage comprises a support vector machine, a rule-based module, and a generalized linear model that estimates the probability of obesity. The second stage comprises a regression neural network that operates on the output of the first stage and a subset of the input feature vector. Cost and utilizations in these areas, along with overall health measures as well as demographics and social factors, are inputs to a set of pattern recognition engines that perform regression. The output is the estimated body mass index of the member.