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    • 8. 发明授权
    • Medical diagnostic and predictive therapeutic method using discriminant analysis
    • 使用判别分析的医学诊断和预测治疗方法
    • US08880174B1
    • 2014-11-04
    • US12844707
    • 2010-07-27
    • Thomas AbellWilliam D. JohnsonHani Rashed
    • Thomas AbellWilliam D. JohnsonHani Rashed
    • A61N1/00
    • A61B5/4848G06F19/00G16H50/30G16H50/70
    • A diagnostic method and predictor of prognosis from various therapeutic treatments is provided using discriminant analysis statistics. In one form, a patient is identified as having a disease using discriminant analysis when one or more values of a physiological parameter of the patient is closer to that of a previously characterized group of individuals having a disease and the patient is diagnosed as not having the disease, or healthy, if the value of a patient's parameter is closer to that of individuals previously characterized as healthy, or not having the disease. For example, the parameters can be based on the autonomic and/or enteric nervous system. Advantageously, the present method can be readily adapted using conventional linear discriminant analysis statistics to factor more than one parameter between a patient and one or more previously classified groups, to thereby enhance predictability and reliability of the present method. Further, in another form, linear discriminant analysis is used to predict outcomes of various therapeutic treatments of a disease to which a patient is afflicted by comparing the value or values of one or more patient parameter with respective ones in previously characterized individuals having the same affliction which have been treated either successfully or unsuccessfully.
    • 使用判别分析统计提供了各种治疗方法的预后的诊断方法和预测指标。 在一种形式中,当患者的生理参数的一个或多个值更接近具有疾病的先前表征的个体组的患者的一个或多个值时,患者被鉴定为具有疾病,并且所述患者被诊断为不具有 疾病或健康,如果患者参数的价值更接近于以前被确定为健康或不具有该疾病的个体的价值。 例如,参数可以基于自主神经和/或肠神经系统。 有利地,本方法可以容易地使用传统的线性判别分析统计量来适应因子以决定患者与一个或多个先前分类的组之间的多于一个参数,从而提高本方法的可预测性和可靠性。 此外,在另一种形式中,线性判别分析用于通过将一个或多个患者参数的值或值与先前具有相同痛苦的先前表征的个体中的相应患者参数进行比较来预测患者所患疾病的各种治疗性治疗的结果 已被成功或不成功地对待。
    • 10. 发明授权
    • Method for steady-state identification based upon identified dynamics
    • 基于确定的动力学的稳态识别方法
    • US6047221A
    • 2000-04-04
    • US943489
    • 1997-10-03
    • Stephen PicheJames David KeelerEric HartmanWilliam D. JohnsonMark GerulesKadir Liano
    • Stephen PicheJames David KeelerEric HartmanWilliam D. JohnsonMark GerulesKadir Liano
    • G05B23/02G05B13/02
    • G05B17/02G05B13/048
    • A method for modeling a steady-state network in the absence of steady-state historical data. A steady-state neural network can be tied by impressing the dynamics of the system onto the input data during the training operation by first determining the dynamics in a local region of the input space, this providing a set of dynamic training data. This dynamic training data is then utilized to train a dynamic model, gain thereof then set equal to unity such that the dynamic model is now valid over the entire input space. This is a linear model, and the historical data over the entire input space is then processed through this model prior to input to the neural network during training thereof to remove the dynamic component from the data, leaving the steady-state component for the purpose of training. This provides a valid model in the presence of historical data that has a large content of dynamic behavior. A single dynamic model is required for each output variable in a multi-input multi-output steady-state model such that for each output there is a separate dynamic model required for pre-filtering. They are combined in a single network made up of multiple individual steady-state models for each output. The dynamic model can be identified utilizing a weighting factor for the gain to force the dynamic gain of the dynamic model to the steady-state gain by weighting the difference thereof during optimization of the dynamic model. The steady-state model is optimized utilizing gain constraints during the optimization procedure such that the gain of the network is prevented from exceeding the gain constraints.
    • 在没有稳态历史数据的情况下建模稳态网络的方法。 稳态神经网络可以通过在训练操作期间通过首先确定输入空间的局部区域中的动力学来将系统的动力学压印到输入数据上,从而提供一组动态训练数据。 然后利用该动态训练数据来训练动态模型,然后将其增益设置为等于1,使动态模型现在在整个输入空间上有效。 这是一个线性模型,然后在整个输入空间中的历史数据在通过该模型输入到神经网络之前通过该模型进行处理,以在训练期间从数据中移除动态分量,将稳态分量留在目的 训练。 这在存在具有大量动态行为的历史数据的情况下提供了有效的模型。 在多输入多输出稳态模型中,每个输出变量都需要单个动态模型,因此对于每个输出,都需要一个单独的动态模型来进行预滤波。 它们组合在由每个输出的多个单独稳态模型组成的单个网络中。 可以利用增益的加权因子来识别动态模型,以通过在动态模型的优化期间加权其差异来将动态模型的动态增益强制为稳态增益。 在优化过程中利用增益约束优化稳态模型,使得网络的增益被阻止超过增益约束。