Cited By
View all- Haque MTozal M(2023)Identification of Fraudulent Healthcare Claims Using Fuzzy Bipartite Knowledge GraphsIEEE Transactions on Services Computing10.1109/TSC.2023.329678216:6(3931-3945)Online publication date: Nov-2023
Negative Insurance Claim Generation Using Distance Pooling on Positive Diagnosis-Procedure Bipartite Graphs
Article No.: 17, Pages 1 - 26
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. 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. 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.
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Index Terms
- Negative Insurance Claim Generation Using Distance Pooling on Positive Diagnosis-Procedure Bipartite Graphs
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Publication History
Published: 23 May 2022
Online AM: 18 April 2022
Accepted: 01 January 2022
Revised: 01 September 2021
Received: 01 May 2020
Published in JDIQ Volume 14, Issue 3
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View all- Haque MTozal M(2023)Identification of Fraudulent Healthcare Claims Using Fuzzy Bipartite Knowledge GraphsIEEE Transactions on Services Computing10.1109/TSC.2023.329678216:6(3931-3945)Online publication date: Nov-2023
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