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US07725329B2 System and method for automatic generation of a hierarchical tree network and the use of two complementary learning algorithms, optimized for each leaf of the hierarchical tree network 有权
用于自动生成分层树网络的系统和方法以及针对分层树网络的每一叶优化的两种补充学习算法的使用

  • Patent Title: System and method for automatic generation of a hierarchical tree network and the use of two complementary learning algorithms, optimized for each leaf of the hierarchical tree network
  • Patent Title (中): 用于自动生成分层树网络的系统和方法以及针对分层树网络的每一叶优化的两种补充学习算法的使用
  • Application No.: US11114692
    Application Date: 2005-04-26
  • Publication No.: US07725329B2
    Publication Date: 2010-05-25
  • Inventor: David H. Kil , David B. Pottschmidt
  • Applicant: David H. Kil , David B. Pottschmidt
  • Applicant Address: US KY Louisville
  • Assignee: Humana Inc.
  • Current Assignee: Humana Inc.
  • Current Assignee Address: US KY Louisville
  • Agency: Greenbaum Doll & McDonald PLLC
  • Agent James C. Eaves, Jr.
  • Main IPC: G06Q10/00
  • IPC: G06Q10/00 ;  G06F19/00 ;  G06Q40/00
System and method for automatic generation of a hierarchical tree network and the use of two complementary learning algorithms, optimized for each leaf of the hierarchical tree network
Abstract:
A system and method that generates a hierarchical tree network and uses linear-plus-nonlinear learning algorithms to form a consensus view on a member's future health status. Each leaf in the hierarchical tree network is homogeneous in clinical characteristics, experience period, and available data assets. Optimization is performed on each leaf so that features and learning algorithms can be tailored to local characteristics specific to each leaf.
Abstract(Chinese):

一种生成层次树网络并使用线性加非线性学习算法来形成对成员未来健康状况的共识视图的系统和方法。 分层树网络中的每个叶片在临床特征，经验期和可用数据资产中是同质的。 在每个叶子上进行优化，使得特征和学习算法可以根据每个叶子特定的局部特征进行调整。

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