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相关概念视频

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As the name suggests, a multiple bar graph is the same as a bar graph but has multiple bars to depict relationships between different data values. One can include as many parameters as possible. However, each parameter must have the same unit of measurement.
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The issues and trends in healthcare delivery are constantly changing. The COVID-19 pandemic is one recent issue that wreaked havoc on healthcare systems, causing a shortage of healthcare workers, high demand for medicines and supplies, and increased medical expenditure due to a lack of insurance. Other issues include rising healthcare costs and care fragmentation.
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相关实验视频

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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
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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.

关键词:
欺诈检测 欺诈检测 欺诈检测图表 卷积网络 卷积网络图形结构学习学习 图形结构学习医疗保险的健康保险是什么不同质的图形神经网络的神经网络.

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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.