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基于多层内核自编码器极端学习机器模型的异常登记检测研究.

Zhengmin Gu1, Lang Guo2, Jie Huang3

  • 1Department of Information Center, The First Hospital of China Medical University, Shenyang, 110002, China. guzm@cmu1h.com.

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概括

这项研究介绍了DKELM-PSS,一种机器学习算法,可以使用健康信息系统数据检测异常的医院注册行为. 它改善了医疗资源的分配和医院的数字化.

关键词:
异常检测检测异常检测核心极端学习机器的核心.机器学习 机器学习萨尔普群群算法 萨尔普群群算法

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科学领域:

  • 医疗信息学 医疗信息学
  • 人工智能的人工智能
  • 机器学习 机器学习

背景情况:

  • 医院的数字化转型是由人工智能和健康信息学推动的.
  • 不当的注册实践导致医疗预约资源的分配不均.

研究的目的:

  • 提出一个集成的机器学习算法 (DKELM-PSS),用于检测医院健康信息系统 (HIS) 中的异常注册行为.
  • 加强医疗资源的分配,促进智能医院的发展.

主要方法:

  • 使用Sparse主要组件分析 (Sparse PCA) 进行数据预处理,以消除和减少维度.
  • 使用Deep Kernel极端学习机器 (DKELM) 模型与堆叠的Kernel极端学习机器自编码器 (KELM-AE) 进行深度特征提取.
  • 使用Salp Swarm算法 (SSA) 进行最佳参数配置,以提高分类准确性和稳定性.

主要成果:

  • 在HIS异常检测中,DKELM-PSS实现了0.9942的最佳精度.
  • 实验结果验证了DKELM-PSS与SVM-RBF,XGBoost和ResNet相比的有效性和稳定性.
  • 该方法在识别异常注册行为方面表现出卓越的性能.

结论:

  • 拟议的DKELM-PSS为医院HIS数据提供了一种高效的异常检测方法.
  • 这种方法有利于优化医疗资源分配.
  • 该研究通过先进的AI技术,为医院服务的智能发展做出了贡献.