Negative Insurance Claim Generation Using Distance Pooling on Positive Diagnosis-Procedure Bipartite Graphs

Abstract

Negative samples in health and medical insurance domain refer to fraudulent or erroneous insurance claims that may include inconsistent diagnosis-procedure relations with respect to a medical coding system. Unfortunately, only a few datasets are publicly available for research in health insurance domain, yet none reports any negative claims. On the other hand, negative claims are essential not only to develop new machine learning approaches, but also to test and validate automated artificial intelligence systems deployed by insurance providers. In this study, we introduce a synthetic negative claim generation procedure based on the bipartite graph representations of positive claims. Our empirical results demonstrate promising outcomes that will improve the development and evaluation processes of machine learning approaches in healthcare, where negative samples are required, but not available. Moreover, the proposed scheme can be applied to other domains, where bipartite graph representations are meaningful and negative samples are lacking.