Integrating Neural Networks for Risk‐Adjustment Models

This article demonstrates the possibility of an alternative approach for risk‐adjustment models. In the proposed model the risk characteristics of the beneficiary's health within the same cohort classified by Self‐Organizing Map network are highly homogeneous, whereas the numbers of individuals within each cohort remain sufficient to allow further investigation of the causal effect from clustered data. A comparison of different models by the 10‐fold cross‐validation reveals that the performance improvement in the proposed integration model is both significant and stable across the estimation and validation sampling.