Research Findings from Xiamen University of Technology Update Understanding of Data Networks (Health insurance fraud detection by using an attributed heterogeneous information network with a hierarchical attention mechanism): Information Technology – Data Networks

Health Policy and Law Daily

2023 APR 24 (NewsRx) -- By a News Reporter-Staff News Editor at Health Policy and Law Daily -- Investigators publish new report on data networks. According to news originating from Xiamen University of Technology by NewsRx correspondents, research stated, “With the rapid growth of healthcare services, health insurance fraud detection has become an important measure to ensure efficient use of public funds. Traditional fraud detection methods have tended to focus on the attributes of a single visit and have ignored the behavioural relationships of multiple visits by patients.”

Funders for this research include The Xmut Scientific Research Project; The Science Foundation of Fujian Province; The Open Fund of Engineering Research Center of Big Data Application in Private Health Medicine,fujian Provincial University.

Our news editors obtained a quote from the research from Xiamen University of Technology: “We propose a health insurance fraud detection model based on a multilevel attention mechanism that we call MHAMFD. Specifically, we use an attributed heterogeneous information network (AHIN) to model different types of objects and their rich attributes and interactions in a healthcare scenario. MHAMFD selects appropriate neighbour nodes based on the behavioural relationships at different levels of a patient’s visit. We also designed a hierarchical attention mechanism to aggregate complex semantic information from the interweaving of different levels of behavioural relationships of patients. This increases the feature representation of objects and makes the model interpretable by identifying the main factors of fraud. Experimental results using real datasets showed that MHAMFD detected health insurance fraud with better accuracy than existing methods.”

According to the news editors, the research concluded: “Experiment suggests that the behavioral relationships between patients’ multiple visits can also be of great help to detect health care fraud. Subsequent research fraud detection methods can also take into account the different behavioral relationships between patients.”

For more information on this research see: Health insurance fraud detection by using an attributed heterogeneous information network with a hierarchical attention mechanism. BMC Medical Informatics and Decision Making, 2023,23(1):1-17. (BMC Medical Informatics and Decision Making - http://bmcmedinformdecismak.biomedcentral.com). The publisher for BMC Medical Informatics and Decision Making is BMC.

A free version of this journal article is available at https://doi.org/10.1186/s12911-023-02152-0.

Our news journalists report that additional information may be obtained by contacting Jiangtao Lu, School of Computer and Information Engineering, Xiamen University of Technology. Additional authors for this research include Kaibiao Lin, Ruicong Chen, Min Lin, Xin Chen, Ping Lu.

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