会员体验
专利管家(专利管理)
工作空间(专利管理)
风险监控(情报监控)
数据分析(专利分析)
侵权分析(诉讼无效)
联系我们
交流群
官方交流:
QQ群: 891211   
微信请扫码    >>>
现在联系顾问~
热词
    • 3. 发明申请
    • PATENT SCORING AND CLASSIFICATION
    • 专利分级和分类
    • US20120296835A1
    • 2012-11-22
    • US13575126
    • 2010-01-25
    • Arif Khan KRahul Jindal
    • Arif Khan KRahul Jindal
    • G06Q50/26
    • G06Q50/18
    • A method, system, and apparatus for classifying intangible assets are provided. The method includes determining an objective of classification. The method further includes constructing, via a processor, a Discriminant Analysis (DA) model using one or more test sets of intangible assets. The DA model includes one or more discriminant functions operable to classify the one or more test set of intangible assets into two or more groups based on a set of attributes associated with one or more intangible assets of the test set of intangible assets to meet the objective of classification. Thereafter, the method includes classifying a target set of intangible assets via the DA model.
    • 提供了分类无形资产的方法,系统和设备。 该方法包括确定分类目标。 该方法还包括通过处理器,使用一个或多个无形资产测试集来构建判别分析(DA)模型。 DA模型包括一个或多个判别函数,可操作以基于与无形资产的测试集合的一个或多个无形资产相关联的一组属性来将无形资产的一个或多个测试集合分成两个或更多个组,以满足目标 的分类。 此后,该方法包括通过DA模型对目标无形资产集进行分类。
    • 5. 发明申请
    • BAYESIAN MODELING OF PRE-TRANSPLANT VARIABLES ACCURATELY PREDICTS KIDNEY GRAFT SURVIVAL
    • 预先准确预测的贝叶斯模型准确预测KIDNEY GRAFT SURVIVAL
    • US20160206249A9
    • 2016-07-21
    • US13662456
    • 2012-10-27
    • Eric A. ElsterDoug TadakiTrevor S. BrownRahul Jindal
    • Eric A. ElsterDoug TadakiTrevor S. BrownRahul Jindal
    • A61B5/00
    • A61B5/7267A61B5/201A61B5/4848A61B5/7275G06F19/00G16H10/60G16H50/20G16H50/50
    • An embodiment of the invention provides a method for determining a patient-specific probability of renal transplant survival. The method collects clinical parameters from a plurality of renal transplant donor and patient to create a training database. A fully unsupervised Bayesian Belief Network model is created using data from the training database; and, the fully unsupervised Bayesian Belief Network is validated. Clinical parameters are collected from an individual patient/donor; and, such clinical parameters are input into the fully unsupervised Bayesian Belief Network model via a graphical user interface. The patient-specific probability of disease is output from the fully unsupervised Bayesian Belief Network model and sent to the graphical user interface for use by a clinician in pre-operative organ matching. The fully unsupervised Bayesian Belief Network model is updated using the clinical parameters from the individual patient and the patient-specific probability of transplant survival.
    • 本发明的一个实施例提供了一种用于确定患者特异性肾移植存活概率的方法。 该方法从多个肾移植供体和患者收集临床参数以创建训练数据库。 使用培训数据库中的数据创建完全无监督的贝叶斯信念网络模型; 并且,完全无监督的贝叶斯信仰网络得到验证。 从个体患者/供体收集临床参数; 并且通过图形用户界面将这样的临床参数输入到完全无监督的贝叶斯信仰网络模型中。 患者特异性疾病概率是从完全无监督的贝叶斯信念网络模型输出的,并发送到图形用户界面,供临床医生在术前器官匹配中使用。 使用来自个体患者的临床参数和患者特异性移植生存概率来更新完全无监督的贝叶斯信念网络模型。
    • 8. 发明授权
    • Bayesian modeling of pre-transplant variables accurately predicts kidney graft survival
    • 移植前变量的贝叶斯模型准确预测肾移植物存活
    • US09561006B2
    • 2017-02-07
    • US13662456
    • 2012-10-27
    • Eric A. ElsterDoug TadakiTrevor S. BrownRahul Jindal
    • Eric A. ElsterDoug TadakiTrevor S. BrownRahul Jindal
    • G06F15/18A61B5/00A61B5/20G06F19/00
    • A61B5/7267A61B5/201A61B5/4848A61B5/7275G06F19/00G16H10/60G16H50/20G16H50/50
    • An embodiment of the invention provides a method for determining a patient-specific probability of renal transplant survival. The method collects clinical parameters from a plurality of renal transplant donor and patient to create a training database. A fully unsupervised Bayesian Belief Network model is created using data from the training database; and, the fully unsupervised Bayesian Belief Network is validated. Clinical parameters are collected from an individual patient/donor; and, such clinical parameters are input into the fully unsupervised Bayesian Belief Network model via a graphical user interface. The patient-specific probability of disease is output from the fully unsupervised Bayesian Belief Network model and sent to the graphical user interface for use by a clinician in pre-operative organ matching. The fully unsupervised Bayesian Belief Network model is updated using the clinical parameters from the individual patient and the patient-specific probability of transplant survival.
    • 本发明的一个实施例提供了一种用于确定患者特异性肾移植存活概率的方法。 该方法从多个肾移植供体和患者收集临床参数以创建训练数据库。 使用培训数据库中的数据创建完全无监督的贝叶斯信念网络模型; 并且,完全无监督的贝叶斯信仰网络得到验证。 从个体患者/供体收集临床参数; 并且通过图形用户界面将这样的临床参数输入到完全无监督的贝叶斯信仰网络模型中。 患者特异性疾病概率是从完全无监督的贝叶斯信念网络模型输出的,并发送到图形用户界面,供临床医生在术前器官匹配中使用。 使用来自个体患者的临床参数和患者特异性移植生存概率来更新完全无监督的贝叶斯信念网络模型。
    • 10. 发明申请
    • BAYESIAN MODELING OF PRE-TRANSPLANT VARIABLES ACCURATELY PREDICTS KIDNEY GRAFT SURVIVAL
    • 预先准确预测的贝叶斯模型准确预测KIDNEY GRAFT SURVIVAL
    • US20140122382A1
    • 2014-05-01
    • US13662456
    • 2012-10-27
    • Eric A. ElsterDoug TadakiTrevor S. BrownRahul Jindal
    • Eric A. ElsterDoug TadakiTrevor S. BrownRahul Jindal
    • A61B5/00
    • A61B5/7267A61B5/201A61B5/4848A61B5/7275G06F19/00G16H10/60G16H50/20G16H50/50
    • An embodiment of the invention provides a method for determining a patient-specific probability of renal transplant survival. The method collects clinical parameters from a plurality of renal transplant donor and patient to create a training database. A fully unsupervised Bayesian Belief Network model is created using data from the training database; and, the fully unsupervised Bayesian Belief Network is validated. Clinical parameters are collected from an individual patient/donor; and, such clinical parameters are input into the fully unsupervised Bayesian Belief Network model via a graphical user interface. The patient-specific probability of disease is output from the fully unsupervised Bayesian Belief Network model and sent to the graphical user interface for use by a clinician in pre-operative organ matching. The fully unsupervised Bayesian Belief Network model is updated using the clinical parameters from the individual patient and the patient-specific probability of transplant survival.
    • 本发明的一个实施例提供了一种用于确定患者特异性肾移植存活概率的方法。 该方法从多个肾移植供体和患者收集临床参数以创建训练数据库。 使用培训数据库中的数据创建完全无监督的贝叶斯信念网络模型; 并且,完全无监督的贝叶斯信仰网络得到验证。 从个体患者/供体收集临床参数; 并且通过图形用户界面将这样的临床参数输入到完全无监督的贝叶斯信仰网络模型中。 患者特异性疾病概率是从完全无监督的贝叶斯信念网络模型输出的,并发送到图形用户界面,供临床医生在术前器官匹配中使用。 使用来自个体患者的临床参数和患者特异性移植生存概率来更新完全无监督的贝叶斯信念网络模型。