Study Findings from University of Louisiana at Lafayette Broaden Understanding of Machine Learning (Negative Insurance Claim Generation Using Distance Pooling on Positive Diagnosis-Procedure Bipartite Graphs): Machine Learning

2023 JAN 16 (NewsRx) -- By a News Reporter-Staff News Editor at Health Policy and Law Daily -- A new study on artificial intelligence is now available. According to news reporting out of Lafayette, Louisiana, by NewsRx editors, research stated, “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.”

The news reporters obtained a quote from the research from University of Louisiana at Lafayette: “Unfortunately, only a few datasets are publicly available for research in health insurance domain, yet none reports any negative claims. However, 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.”

According to the news editors, the research concluded: “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.”

For more information on this research see: Negative Insurance Claim Generation Using Distance Pooling on Positive Diagnosis-Procedure Bipartite Graphs. Journal of Data and Information Quality, 2022,14(3). The publisher for Journal of Data and Information Quality is Association for Computing Machinery (ACM).

A free version of this journal article is available at https://doi.org/10.1145/3531347.

Our news journalists report that more information may be obtained by contacting Md Enamul Haque, School of Computing and Informatics, University of Louisiana at Lafayette, Lafayette, Louisiana, United States. Additional authors for this research include Mehmet Engin Tozal.

ORCID is an identifier for authors and includes bibliographic information. The following is ORCID information for the authors of this research: Md Enamul Haque (http://orcid.org/0000-0002-1215-9647), Mehmet Engin Tozal (http://orcid.org/0000-0003-2157-2780).

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