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1. 发明申请

US20130065605A1 KL-Divergence Kernel Regression For Non-Gaussian Fingerprint Based Localization 有权
标题翻译：基于非高斯指纹定位的KL发散内核回归
公开(公告)号：US20130065605A1
公开(公告)日：2013-03-14
申请号：US13249895
申请日：2011-09-30
申请人： Piotr Mirowski , Harald Steck , Philip A. Whiting , Ravishankar Palaniappan , William Michael MacDonald , Tin Kam Ho
发明人： Piotr Mirowski , Harald Steck , Philip A. Whiting , Ravishankar Palaniappan , William Michael MacDonald , Tin Kam Ho
IPC分类号： H04W24/00
CPC分类号： G01S5/0252 , G01S5/0278 , G06N99/005
摘要： Embodiments are directed to mobile localization, and more specifically, but not exclusively, to tracking mobile devices. Embodiments include methods that consider probability kernels with distance-like metrics between distributions. Also described are probabilistic kernels that can be used for a regression of location, which can achieve up to about inn accuracy in an office environment.
摘要翻译：实施例涉及移动定位，并且更具体地但不排他地涉及跟踪移动设备。实施例包括在分布之间考虑具有距离的度量的概率内核的方法。还描述了可以用于位置回归的概率内核，其可以在办公室环境中实现高达约精确度。

2. 发明授权

US07805385B2 Prognosis modeling from literature and other sources 有权
标题翻译：文献和其他来源的预后建模
公开(公告)号：US07805385B2
公开(公告)日：2010-09-28
申请号：US11735736
申请日：2007-04-16
申请人： Harald Steck , Sriram Krishnan , R. Bharat Rao , Philippe Lambin , Cary Dehing-Oberije
发明人： Harald Steck , Sriram Krishnan , R. Bharat Rao , Philippe Lambin , Cary Dehing-Oberije
IPC分类号： G06E1/00 , G06F15/00 , G06G7/00 , G06N99/00
CPC分类号： G16H50/20 , G06F19/00 , G16H50/70
摘要： A predictor of medical treatment outcome is developed and applied. A prognosis model is developed from literature. The model is determined by reverse engineering the literature reported quantities. A relationship of a given variable to a treatment outcome is derived from the literature. A processor may then use individual patient values for one or more variables to predict outcome. The accuracy may be increased by including a data driven model in combination with the literature driven model.
摘要翻译：开发并应用了治疗结果的预测因子。从文献开发预后模型。该模型通过逆向工程文献报道的量来确定。给定变量与治疗结果的关系来源于文献。然后，处理器可以使用单个患者值用于一个或多个变量来预测结果。通过将数据驱动模型与文献驱动模型相结合，可以提高精度。

3. 发明申请

US20100131438A1 Medical Ontologies for Computer Assisted Clinical Decision Support 有权
标题翻译：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。

4. 发明申请

US20080033894A1 Prognosis Modeling From One or More Sources of Information 有权
标题翻译：从一个或多个信息来源的预测建模
公开(公告)号：US20080033894A1
公开(公告)日：2008-02-07
申请号：US11735736
申请日：2007-04-16
申请人： Harald Steck , Sriram Krishnan , R. Rao , Philippe Lambin , Cary Dehing-Oberije
发明人： Harald Steck , Sriram Krishnan , R. Rao , Philippe Lambin , Cary Dehing-Oberije
IPC分类号： G06F15/18
CPC分类号： G16H50/20 , G06F19/00 , G16H50/70
摘要： A predictor of medical treatment outcome is developed and applied. A prognosis model is developed from literature. The model is determined by reverse engineering the literature reported quantities. A relationship of a given variable to a treatment outcome is derived from the literature. A processor may then use individual patient values for one or more variables to predict outcome. The accuracy may be increased by including a data driven model in combination with the literature driven model.
摘要翻译：开发并应用了治疗结果的预测因子。从文献开发预后模型。该模型通过逆向工程文献报道的量来确定。给定变量与治疗结果的关系来源于文献。然后，处理器可以使用单个患者值用于一个或多个变量来预测结果。通过将数据驱动模型与文献驱动模型相结合，可以提高精度。

5. 发明申请

US20070192143A1 Quality Metric Extraction and Editing for Medical Data 有权
标题翻译：医学数据质量公制提取和编辑
公开(公告)号：US20070192143A1
公开(公告)日：2007-08-16
申请号：US11672639
申请日：2007-02-08
申请人： Sriram Krishnan , William Landi , Harald Steck , Romer Rosales , Radu Niculescu , Farbod Rahmanian , R. Rao
发明人： Sriram Krishnan , William Landi , Harald Steck , Romer Rosales , Radu Niculescu , Farbod Rahmanian , R. Rao
IPC分类号： G06F19/00 , A61B5/00
CPC分类号： G06Q30/04 , G06F19/00 , G06Q10/10 , G06Q50/22 , G06Q50/24
摘要： Medical related quality of care information is extracted and edited for reporting. Patient records are mined. The mining may include mining unstructured data to create structured information. Measures are derived automatically from the structured information. A user may then edit the measures, data points used to derive the measures, or other quality metric based on expert review. The editing may allow for a better quality report. Tools may be provided to configure reports, allowing generation of new or different reports.
摘要翻译：提取和编辑医疗相关的护理信息质量报告。开采病人记录。挖掘可能包括挖掘非结构化数据以创建结构化信息。措施是从结构化信息自动导出的。然后，用户可以基于专家评审来编辑用于导出度量的度量，数据点或其他质量度量。编辑可能允许更好的质量报告。可以提供工具来配置报告，允许生成新的或不同的报告。

6. 发明申请

US20140047042A1 METHOD AND A SERVER FOR ROUTING BETWEEN DEVICES OF A COMPUTER BASED SOCIAL NETWORK 审中-公开
标题翻译：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）。

7. 发明授权

US07630947B2 Medical ontologies for computer assisted clinical decision support 有权
标题翻译：用于计算机辅助临床决策支持的医学本体
公开(公告)号：US07630947B2
公开(公告)日：2009-12-08
申请号：US11505080
申请日：2006-08-16
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： G06F17/00 , G06N5/02
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。

8. 发明申请

US20090092300A1 System and Method for Lesion Detection Using Locally Adjustable Priors 有权
标题翻译：使用局部可调节的病变检测系统和方法
公开(公告)号：US20090092300A1
公开(公告)日：2009-04-09
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
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。

9. 发明申请

US20080301077A1 System and Method for Medical Predictive Models Using Likelihood Gamble Pricing 有权
标题翻译：使用似然Gamble定价的医学预测模型的系统和方法
公开(公告)号：US20080301077A1
公开(公告)日：2008-12-04
申请号：US12128947
申请日：2008-05-29
申请人： Glenn Fung , Phan Hong Giang , Harald Steck , R. Bharat Rao
发明人： Glenn Fung , Phan Hong Giang , Harald Steck , R. Bharat Rao
IPC分类号： G06N5/04
CPC分类号： G06K9/6278 , G06F19/00 , G16H50/20
摘要： A method for predicting survival rates of medical patients includes providing a set D of survival data for a plurality of medical patients, providing a regression model having an associated parameter vector β, providing an example x0 of a medical patient whose survival probability is to be classified, calculating a parameter vector {circumflex over (β)} that maximizes a log-likelihood function of β over the set of survival data, l(β|D), wherein the log likelihood l(β|D) is a strictly concave function of β and is a function of the scalar xβ, calculating a weight w0 for example x0, calculating an updated parameter vector β* that maximizes a function l(β|D∪{(y0,x0,w0)}), wherein data points (y0,x0,w0) augment set D, calculating a fair log likelihood ratio λƒ from {circumflex over (β)} and β* using λƒ=λ(β*|x0)+sign(λ({circumflex over (β)}|x0)){l({circumflex over (β)}|D)−l(β*|D)}, and mapping the fair log likelihood ratio λƒ to a fair price y0ƒ, wherein said fair price is a probability that class label y0 for example x0 has a value of 1.
摘要翻译：一种用于预测医疗患者的存活率的方法包括为多个医疗患者提供生存数据的集合D，提供具有相关联的参数向量β的回归模型，提供其生存概率被分类的医疗患者的示例x0 计算生存数据集合l（β| D）使β的对数似然函数最大化的参数向量{circumflex over（beta）}，其中对数似然l（β| D）是严格凹函数并且是标量xbeta的函数，计算例如x0的权重w0，计算最大化函数l（β|D∪{（y0，x0，w0）}）的更新参数向量β*，其中数据点（y0，x0，w0）增加集合D，使用lambdaf = lambda（beta * | x0）+ sign（lambda（{circumflex over（beta））从{circumflex over（beta）}和beta *计算公平对数似然比lambdaf } | x0））{l（{circumflex over（beta）} | D）-l（beta * | D）}，并映射公平对数似然比 mbdaf以公平价格y0f，其中所述公平价格是类标签y0，例如x0具有值1的概率。

10. 发明申请

US20080059391A1 Learning Or Inferring Medical Concepts From Medical Transcripts 有权
标题翻译：从医学成绩单学习或推理医学概念
公开(公告)号：US20080059391A1
公开(公告)日：2008-03-06
申请号：US11850193
申请日：2007-09-05
申请人： Romer Rosales , Praveen Krishnamurthy , R. Rao , Harald Steck
发明人： Romer Rosales , Praveen Krishnamurthy , R. Rao , Harald Steck
IPC分类号： G06F15/18 , G06F17/10
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。可以采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。该模型基于医学成绩单的类型，重点在于该数据来源以输出患者的最可能的状态医疗领域或领域。所学习的模型可以用于推断患者的医学概念的状态

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