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    METHOD AND A SERVER FOR ROUTING BETWEEN DEVICES OF A COMPUTER BASED SOCIAL NETWORK
    1.
    发明申请
    METHOD AND A SERVER FOR ROUTING BETWEEN DEVICES OF A COMPUTER BASED SOCIAL NETWORK 审中-公开
    方法和用于在基于计算机的社交网络的设备之间路由的服务器

    公开(公告)号：US20140047042A1

    公开(公告)日：2014-02-13

    申请号：US13571967

    申请日：2012-08-10

    申请人： Harald Steck ,  Xiwang Yang ,  Yong Liu

    发明人： Harald Steck ,  Xiwang Yang ,  Yong Liu

    IPC分类号： H04L12/58

    CPC分类号： H04L51/32

    摘要： The invention concerns a method and a server for routing between devices of a computer based social network having a plurality of users, wherein upon receipt of a first message from a device (110) associated with a user (u1), a second message is sent to another device (104, 110), wherein the other device (104, 110) is selected from a plurality of predetermined devices depending on the result of an evaluation of at least 1 trust value (S(c,u1;uM)) associated with the user (u1), a category (c) of content of the computer based social network and another user (uM) of the computer based social network.

    摘要翻译： 本发明涉及一种用于在具有多个用户的基于计算机的社交网络的设备之间进行路由的方法和服务器，其中在从与用户（u1）相关联的设备（110）接收到第一消息时，发送第二消息 根据至少1个信任值（S（c，u1，uM））的关联的结果的结果，从多个预定的设备中选择另一设备（104,110） 用户（u1），基于计算机的社交网络的内容的类别（c）和基于计算机的社交网络的另一用户（uM）。

    System and method for lesion detection using locally adjustable priors
    2.
    发明授权
    System and method for lesion detection using locally adjustable priors 有权
    使用局部可调节先验的病变检测系统和方法

    公开(公告)号：US07876943B2

    公开(公告)日：2011-01-25

    申请号：US12241183

    申请日：2008-09-30

    申请人： Anna Jerebko ,  Marcos Salganicoff ,  Maneesh Dewan ,  Harald Steck

    发明人： Anna Jerebko ,  Marcos Salganicoff ,  Maneesh Dewan ,  Harald Steck

    IPC分类号： G06K9/00 ,  A61B5/00

    CPC分类号： G06K9/6278 ,  G06K2209/05 ,  G06K2209/053 ,  G06T7/0012 ,  G06T7/11 ,  G06T7/143 ,  G06T2207/30028 ,  G06T2207/30061

    摘要： According to an aspect of the invention, a method for training a classifier for classifying candidate regions in computer aided diagnosis of digital medical images includes providing a training set of annotated images, each image including one or more candidate regions that have been identified as suspicious, deriving a set of descriptive feature vectors, where each candidate region is associated with a feature vector. A subset of the features are conditionally dependent, and the remaining features are conditionally independent. The conditionally independent features are used to train a naïve Bayes classifier that classifies the candidate regions as lesion or non-lesion. A joint probability distribution that models the conditionally dependent features, and a prior-odds probability ratio of a candidate region being associated with a lesion are determined from the training images. A new classifier is formed from the naïve Bayes classifier, the joint probability distribution, and the prior-odds probability ratio.

    摘要翻译： 根据本发明的一个方面，一种训练分类器的方法，用于对数字医学图像的计算机辅助诊断中的候选区域进行分类，包括提供注释图像的训练集，每个图像包括已被识别为可疑的一个或多个候选区域， 导出一组描述性特征向量，其中每个候选区域与特征向量相关联。 特征的子集有条件依赖，其余的特征是有条件的独立的。 条件独立的特征用于训练将候选区域分类为病变或非损伤的朴素贝叶斯分类器。 从训练图像确定与条件相关特征建模的联合概率分布以及与病变相关联的候选区域的先验概率概率。 从初始贝叶斯分类器，联合概率分布和先验概率概率比构成新的分类器。

    Learning or inferring medical concepts from medical transcripts using probabilistic models with words or phrases identification
    3.
    发明授权
    Learning or inferring medical concepts from medical transcripts using probabilistic models with words or phrases identification 有权
    使用具有单词或短语识别的概率模型从医学成绩单学习或推断医学概念

    公开(公告)号：US07840511B2

    公开(公告)日：2010-11-23

    申请号：US11850193

    申请日：2007-09-05

    申请人： Romer E. Rosales ,  Praveen Krishnamurthy ,  R. Bharat Rao ,  Harald Steck

    发明人： Romer E. Rosales ,  Praveen Krishnamurthy ,  R. Bharat Rao ,  Harald Steck

    IPC分类号： G06N5/00

    CPC分类号： G16H50/20 ,  G06F19/00 ,  G16H50/50

    摘要： A medical concept is learned about or inferred from a medical transcript. A probabilistic model is trained from medical transcripts. For example, the problem is treated as a graphical model. Discrimitive or generative learning is used to train the probabilistic model. A mutual information criterion can be employed to identify a discrete set of words or phrases to be used in the probabilistic model. The model is based on the types of medical transcripts, focusing on this source of data to output the most probable state of a patient in the medical field or domain. The learned model may be used to infer a state of a medical concept for a patient.

    摘要翻译： 从医学成绩单中了解或推断医学概念。 概率模型由医学成绩单进行培训。 例如，该问题被视为图形模型。 使用歧视或生成学习来训练概率模型。 可以使用互信息标准来识别要在概率模型中使用的一组离散的单词或短语。 该模型基于医疗成绩单的类型，重点是这一数据来源，以输出医疗领域或领域患者的最可能状态。 所学习的模型可以用于推断患者的医学概念的状态。

    Recommender System with Training Function Based on Non-Random Missing Data
    5.
    发明申请
    Recommender System with Training Function Based on Non-Random Missing Data 有权
    基于非随机丢失数据的训练函数推荐系统

    公开(公告)号：US20120023045A1

    公开(公告)日：2012-01-26

    申请号：US12841720

    申请日：2010-07-22

    申请人： Harald Steck

    发明人： Harald Steck

    IPC分类号： G06N3/08 ,  G06F15/18

    CPC分类号： G06N99/005

    摘要： A processing device of an information processing system is operative to obtain observed feedback data, to construct a model that accounts for both the observed feedback data and additional feedback data that is missing from the observed feedback data, to optimize one or more parameters of the model using a training objective function, and to generate a list of recommended items for a given user based on the optimized model. In illustrative embodiments, the missing feedback data comprises data that is missing not at random (MNAR), and the model comprises a matrix factorization model. The processing device may implement a recommender system comprising a training module coupled to a recommendation module.

    摘要翻译： 信息处理系统的处理装置可操作以获得观察到的反馈数据，以构建考虑观察到的反馈数据和观察到的反馈数据中缺失的附加反馈数据的模型，以优化模型的一个或多个参数 使用训练目标函数，并基于优化的模型生成给定用户的推荐项目列表。 在说明性实施例中，丢失的反馈数据包括不是随机丢失的数据（MNAR），并且该模型包括矩阵分解模型。 处理装置可以实现包括与推荐模块耦合的训练模块的推荐器系统。

    Medical ontologies for computer assisted clinical decision support
    7.
    发明申请
    Medical ontologies for computer assisted clinical decision support 有权
    用于计算机辅助临床决策支持的医学本体

    公开(公告)号：US20070094188A1

    公开(公告)日：2007-04-26

    申请号：US11505080

    申请日：2006-08-16

    申请人： Abhinay Pandya ,  Romer Rosales ,  R. Rao ,  Harald Steck

    发明人： Abhinay Pandya ,  Romer Rosales ,  R. Rao ,  Harald Steck

    IPC分类号： G06N5/00

    CPC分类号： G16H50/20 ,  G06F19/00

    摘要： Medical ontology information is used for mining and/or probabilistic modeling. A domain knowledge base may be automatically or semi-automatically created by a processor from a medical ontology. The domain knowledge base, such as a list of disease associated terms, is used to mine for corresponding information from a medical record. The relationship of different terms with respect to a disease may be used to train a probabilistic model. Probabilities of a disease or chance of indicating the disease are determined based on the terms from a medical ontology. This probabilistic reasoning is learned with a machine from ontology information and a training data set.

    摘要翻译： 医学本体信息用于挖掘和/或概率建模。 领域知识库可以由处理器从医学本体自动或半自动创建。 领域知识库，例如疾病相关术语列表，用于从医疗记录中挖掘相应的信息。 可以使用不同术语与疾病的关系来训练概率模型。 根据医学本体的术语确定疾病的概率或指示疾病的机会。 这种概率推理是通过本体信息和训练数据集的机器学习的。

    Medical Ontologies for Computer Assisted Clinical Decision Support
    10.
    发明申请
    Medical Ontologies for Computer Assisted Clinical Decision Support 有权
    计算机辅助临床决策支持医学本体

    公开(公告)号：US20100131438A1

    公开(公告)日：2010-05-27

    申请号：US12620353

    申请日：2009-11-17

    申请人： Abhinay Mahesh Pandya ,  Romer E. Rosales ,  R. Bharat Rao ,  Harald Steck

    发明人： Abhinay Mahesh Pandya ,  Romer E. Rosales ,  R. Bharat Rao ,  Harald Steck

    IPC分类号： G06F17/30 ,  G06F15/18

    CPC分类号： G16H50/20 ,  G06F19/00

    摘要： Medical ontology information is used for mining and/or probabilistic modeling. A domain knowledge base may be automatically or semi-automatically created by a processor from a medical ontology. The domain knowledge base, such as a list of disease associated terms, is used to mine for corresponding information from a medical record. The relationship of different terms with respect to a disease may be used to train a probabilistic model. Probabilities of a disease or chance of indicating the disease are determined based on the terms from a medical ontology. This probabilistic reasoning is learned with a machine from ontology information and a training data set.

    摘要翻译： 医学本体信息用于挖掘和/或概率建模。 领域知识库可以由处理器从医学本体自动或半自动创建。 领域知识库，例如疾病相关术语列表，用于从医疗记录中挖掘相应的信息。 可以使用不同术语与疾病的关系来训练概率模型。 根据医学本体的术语确定疾病的概率或指示疾病的机会。 这种概率推理是通过本体信息和训练数据集的机器学习的。