Identification of Fraudulent Healthcare Claims Using Fuzzy Bipartite Knowledge Graphs
Md. Enamul Haque, Mehmet Engin Tozal · IEEE Transactions on Services Computing · 2023
Health insurance is one of the most important services that people depend on for paying the bills related to hospital and clinical services. This dependency on health insurance lures some healthcare service providers to commit insurance frauds which has become a grave concern. The majority of healthcare fraud is committed by a very small number of untrustworthy providers. Yet, such fraudulent actions damage the reputation of the health service providers and cost the system billions of dollars. In this article, we specifically focus on the fraudulent claim identification problem and develop different solution schemes to identify the fraudulent cases in healthcare claims with minimal data. We present a solution to the fraudulent claim identification problem that translates diagnoses and procedure code's relations into Bipartite Graphs with Fuzzy Edges (BiGFuzzE). We also investigate the extension ofBiGFuzzEusing vector representations of clinical codes instead of non-negative matrix factorization (NMF). Our experimental evaluations demonstrate significant outcomes.