基于多道异质图形结构学习的医疗保险欺诈检测
Binsheng Hong1, Ping Lu2, Hang Xu3
1School of Computer and Information Engineering, Xiamen University of Technology, Xiamen, 361024, Fujian Province, China.
Heliyon
|May 2, 2024
概括
医疗保险欺诈的检测得到了新的多道异构图结构学习 (MHGSL) 方法的增强. 这种方法可以准确地识别欺诈性患者,提高系统的公平性和可持续性.
科学领域:
- 计算机科学 计算机科学
- 数据科学数据科学数据科学
- 医疗信息学 医疗信息学
背景情况:
- 医疗保险欺诈正在增加,破坏了系统的公平性和可持续性.
- 传统的欺诈检测方法与复杂,不断变化的数据和欺诈策略作斗争.
- 迫切需要先进的,可适应的分析来有效检测医疗保险欺诈行为.
研究的目的:
- 引入多通道异构图结构化学习 (MHGSL) 方法,用于检测医疗保险欺诈.
- 利用图形结构学习和深度学习来改进欺诈识别.
- 提高医疗保险数据中检测欺诈活动的准确性和效率.
主要方法:
- 从不同的医疗保险实体 (患者,部门,药物) 构建一个异质图.
- 使用图形结构学习来提取拓,特征和语义信息.
- 利用深度学习 (异质图神经网络,图卷积神经网络) 进行多通道信息融合和异常检测.
主要成果:
- MHGSL在检测潜在的医疗保险欺诈方面表现出很高的准确性,优于现有的方法.
- 该方法有效地和快速地识别出表现出欺诈行为的患者.
- 实验证实了多通道异构图形结构学习对欺诈检测有效性的重大贡献.
结论:
- MHGSL为检测医疗保险欺诈提供了一个有前途的解决方案,提高了系统的公平性和可持续性.
- 该方法有效地解决了传统欺诈检测方法的局限性.
- 未来的研究应该探索纳入患者和各种实体之间的语义信息,以进一步改进.
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