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21. 发明授权

US08010476B2 System and method for medical predictive models using likelihood gamble pricing 有权
标题翻译：使用可能性赌博定价的医学预测模型的系统和方法
公开(公告)号：US08010476B2
公开(公告)日：2011-08-30
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： G06F17/00 , G06N5/02
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 λf from {circumflex over (β)} and β* using λf=λ(β*|x0)+sign(λ({circumflex over (β)}|x0)){l({circumflex over (β)}|D)−l(β*|D)}, and mapping the fair log likelihood ratio λf to a fair price y0f, 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，提供具有相关参数向量的回归模型，提供其生存概率为的医疗患者的示例x0 分类，计算最大化对数似然函数＆bgr的参数向量{circumflex over（＆bgr;）} 超过一组生存数据，l（＆bgr; | D），其中对数似然l（＆bgr | | D）是严格的凹函数＆bgr; 并且是标量x＆bgr的函数;计算例如x0的权重w0，计算更新的参数向量＆bgr; *使函数l（＆bgr; |D∪{（y0，x0，w0）}）最大化，其中数据点（y0，x0，w0）增加集合D，使用λf=λ（＆bgr; * | x0）+符号（λ（{circumflex））计算{circumflex over（＆bgr;）}和＆bgr; *的公平对数似然比λf over（＆bgr;）} | x0））{l（{circumflex over（＆bgr;）} | D）-l（＆bgr; * | D）}，并将公平对数似然比λf映射到公平价格y0f，其中公平价格是类标签y0（例如x0）的值为1的概率。

22. 发明授权

US07756313B2 System and method for computer aided detection via asymmetric cascade of sparse linear classifiers 有权
标题翻译：通过稀疏线性分类器的不对称级联进行计算机辅助检测的系统和方法
公开(公告)号：US07756313B2
公开(公告)日：2010-07-13
申请号：US11592869
申请日：2006-11-03
申请人： Jinbo Bi , Senthil Periaswamy , Kazunori Okada , Toshiro Kubota , Glenn Fung , Marcos Salganicoff , R. Bharat Rao
发明人： Jinbo Bi , Senthil Periaswamy , Kazunori Okada , Toshiro Kubota , Glenn Fung , Marcos Salganicoff , R. Bharat Rao
IPC分类号： G06K9/00
CPC分类号： G06K9/6256 , G06K9/6282
摘要： A method for computer aided detection of anatomical abnormalities in medical images includes providing a plurality of abnormality candidates and features of said abnormality candidates, and classifying said abnormality candidates as true positives or false positives using a hierarchical cascade of linear classifiers of the form sign(wTx+b), wherein x is a feature vector, w is a weighting vector and b is a model parameter, wherein different weights are used to penalize false negatives and false positives, and wherein more complex features are used for each successive stage of said cascade of classifiers.
摘要翻译：一种用于计算机辅助检测医学图像中的解剖异常的方法，包括提供所述异常候选的多个异常候选和特征，并且使用形式符号的线性分类器（wTx）的分级级联将所述异常候选分类为真正的肯定或假阳性 + b），其中x是特征向量，w是加权向量，b是模型参数，其中使用不同权重来惩罚假否定和假阳性，并且其中对于级联的每个连续阶段使用更复杂的特征的分类器。

23. 发明授权

US07386165B2 System and method for a sparse kernel expansion for a Bayes classifier 失效
标题翻译：用于Bayes分类器的稀疏内核扩展的系统和方法
公开(公告)号：US07386165B2
公开(公告)日：2008-06-10
申请号：US11049187
申请日：2005-02-02
申请人： Murat Dundar , Glenn Fung , Jinbo Bi , R. Bharat Rao
发明人： Murat Dundar , Glenn Fung , Jinbo Bi , R. Bharat Rao
IPC分类号： G06K9/62 , G06K9/00 , G06E1/00 , G06N3/02
CPC分类号： G06K9/6256
摘要： A method and device having instructions for analyzing input data-space by learning classifiers include choosing a candidate subset from a predetermined training data-set that is used to analyze the input data-space. Candidates are temporarily added from the candidate subset to an expansion set to generate a new kernel space for the input data-space by predetermined repeated evaluations of leave-one-out errors for the candidates added to the expansion set. This is followed by removing the candidates temporarily added to the expansion set after the leave-one-out error evaluations are performed, and selecting the candidates to be permanently added to the expansion set based on the leave-one-out errors of the candidates temporarily added to the expansion set to determine the one or more classifiers.
摘要翻译：具有用于通过学习分类器分析输入数据空间的指令的方法和设备包括从用于分析输入数据空间的预定训练数据集中选择候选子集。将候选者从候选子集临时添加到扩展集合，以通过对添加到扩展集合的候选者的一对一错误进行预先重复的评估来为输入数据空间生成新的内核空间。之后，在执行一次性错误评估之后，删除临时添加到扩展集的候选者，并且基于临时的候选者的一次性错误选择要永久添加到扩展集的候选项添加到扩展集以确定一个或多个分类器。

24. 发明授权

US07512276B2 System and method for feature identification in digital images based on rule extraction 有权
标题翻译：基于规则提取的数字图像中特征识别的系统和方法
公开(公告)号：US07512276B2
公开(公告)日：2009-03-31
申请号：US11145886
申请日：2005-06-06
申请人： Glenn Fung , Sathyakama Sandilya , R. Bharat Rao
发明人： Glenn Fung , Sathyakama Sandilya , R. Bharat Rao
IPC分类号： G06K9/62
CPC分类号： G06K9/6253 , G06K9/626 , G06K9/6269
摘要： A method for classifying features in a digital medical image includes providing a plurality of feature points in an N-dimensional space, wherein each feature point is a member of one of two sets, determining a classifying plane that separates feature points in a first of the two sets from feature points in a second of the two sets, transforming the classifying plane wherein a normal vector to said transformed classifying plane has positive coefficients and a feature domain for one or more feature points of one set is a unit hypercube in a transformed space having n axes, obtaining an upper bound along each of the n-axes of the unit hypercube, inversely transforming said upper bound to obtain a new rule containing one or more feature points of said one set, and removing the feature points contained by said new rule from said one set.
摘要翻译：一种用于对数字医学图像中的特征进行分类的方法包括在N维空间中提供多个特征点，其中每个特征点是两组中的一个的成员，确定分类平面，在两组中的第二组中的特征点中的两组，变换分类平面，其中向所述变换的分类平面的法向量具有正系数，并且一组中的一个或多个特征点的特征域是变换空间中的单位超立方体具有n个轴，获得沿着单位超立方体的每个n轴的上限，逆变换所述上限以获得包含所述一个集合的一个或多个特征点的新规则，以及移除由所述新立体包含的特征点规则来自所述一套。

25. 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。

26. 发明申请

US20060247544A1 Characterization of cardiac motion with spatial relationship 审中-公开
标题翻译：心脏运动与空间关系的表征
公开(公告)号：US20060247544A1
公开(公告)日：2006-11-02
申请号：US11332975
申请日：2006-01-17
申请人： Maleeha Qazi , Mustafa Kamasak , Murat Dundar , Glenn Fung , Sriram Krishnan , R. Rao
发明人： Maleeha Qazi , Mustafa Kamasak , Murat Dundar , Glenn Fung , Sriram Krishnan , R. Rao
IPC分类号： A61B5/02
CPC分类号： G06T7/0012 , A61B5/00 , A61B5/055 , A61B5/7267 , A61B6/03 , A61B6/5217 , A61B8/08 , G06F19/321 , G06K9/00335 , G06K2209/051 , G06T7/20 , G06T2207/30048 , G16H50/20 , G16H50/50
摘要： Cardiac motion is automatically characterized based on spatial relationship to health. A classifier is trained for the characterization of cardiac motion. Regional wall motion abnormality assessment may be improved by combining information from neighboring segments. The structure or relationship between different segments and associated probabilities of different spatial locations being abnormal given another segment being abnormal are used for classification.
摘要翻译：基于与健康的空间关系自动表征心脏运动。训练分类器用于表征心脏运动。可以通过组合来自相邻段的信息来改善区域壁运动异常评估。使用不同分段之间的结构或关系，以及不同空间位置的相关概率在另一段异常的情况下是异常的。

27. 发明申请

US20050049497A1 Systems and methods for automated diagnosis and decision support for breast imaging 有权
标题翻译：用于乳腺成像自动诊断和决策支持的系统和方法
公开(公告)号：US20050049497A1
公开(公告)日：2005-03-03
申请号：US10877129
申请日：2004-06-25
申请人： Sriram Krishnan , R. Rao , Murat Dundar , Glenn Fung
发明人： Sriram Krishnan , R. Rao , Murat Dundar , Glenn Fung
IPC分类号： A61B8/00 , G06F19/00 , G06T7/00
CPC分类号： G06T7/0012 , G06F19/00 , G06F19/321 , G06T2207/10088 , G06T2207/10116 , G06T2207/20084 , G06T2207/30068 , G16H40/63 , G16H50/20 , Y10S128/92 , Y10S128/922 , Y10S128/923 , Y10S128/924
摘要： CAD (computer-aided diagnosis) systems and applications for breast imaging are provided, which implement methods to automatically extract and analyze features from a collection of patient information (including image data and/or non-image data) of a subject patient, to provide decision support for various aspects of physician workflow including, for example, automated diagnosis of breast cancer other automated decision support functions that enable decision support for, e.g., screening and staging for breast cancer. The CAD systems implement machine-learning techniques that use a set of training data obtained (learned) from a database of labeled patient cases in one or more relevant clinical domains and/or expert interpretations of such data to enable the CAD systems to “learn” to analyze patient data and make proper diagnostic assessments and decisions for assisting physician workflow.
摘要翻译：提供了用于乳腺成像的CAD（计算机辅助诊断）系统和应用，其实现了从受试患者的患者信息（包括图像数据和/或非图像数据）的集合中自动提取和分析特征的方法，以提供对医生工作流程的各个方面的决策支持，包括例如乳腺癌的自动诊断其他自动化决策支持功能，其能够为乳腺癌的筛选和分期提供决策支持。 CAD系统实施机器学习技术，其使用从一个或多个相关临床领域的标记的患者病例的数据库获得（学习）的一组训练数据和/或对这些数据的专家解释，使得CAD系统能够“学习” 分析患者数据，进行适当的诊断评估和决策，以协助医师的工作流程。

28. 发明申请

US20070011121A1 System and method for learning rankings via convex hull separation 审中-公开
公开(公告)号：US20070011121A1
公开(公告)日：2007-01-11
申请号：US11444606
申请日：2006-06-01
申请人： Jinbo Bi , Glenn Fung , Sriram Krishnan , Balaji Krishnapuram , R. Rao , Romer Rosales
发明人： Jinbo Bi , Glenn Fung , Sriram Krishnan , Balaji Krishnapuram , R. Rao , Romer Rosales
IPC分类号： G06F15/18
CPC分类号： G06K9/6269
摘要： A method for finding a ranking function ƒ that classifies feature points in an n-dimensional space includes providing a plurality of feature points xk derived from tissue sample regions in a digital medical image, providing training data A comprising training samples Aj where A = ⋃ j = 1 S ⁢ ( A j = { x i j } i = 1 m j ) , providing an ordering E={(P,Q)|APAQ} of at least some training data sets where all training samples xiεAP are ranked higher than any sample xjεAQ, solving a mathematical optimization program to determine the ranking function ƒ that classifies said feature points x into sets A. For any two sets Ai, Aj, AiAj, and the ranking function ƒ satisfies inequality constraints ƒ(xi)≦ƒ(xj) for all xiεconv(Ai) and xjεconv(Aj), where conv(A) represents the convex hull of the elements of set A.

29. 发明申请

US20140095205A1 AUTOMATED MAPPING OF SERVICE CODES IN HEALTHCARE SYSTEMS 审中-公开
标题翻译：医疗卫生系统服务代码的自动映射
公开(公告)号：US20140095205A1
公开(公告)日：2014-04-03
申请号：US14037548
申请日：2013-09-26
申请人： Faisal Farooq , Marc Overhage , Glenn Fung , Shipeng Yu , Bharat R. Rao , Balaji Krishnapuram , Jan DeHaan
发明人： Faisal Farooq , Marc Overhage , Glenn Fung , Shipeng Yu , Bharat R. Rao , Balaji Krishnapuram , Jan DeHaan
IPC分类号： G06F19/00
CPC分类号： G16H10/60
摘要： Automatic mapping of semantics in healthcare is provided. Data sets have different semantics (e.g., Gender designated with M and F in one system and Sex designated with 1 or 2 in another system). For semantic interoperability, the semantic links between the semantic systems of different healthcare entities are created (e.g., Gender=Sex and/or 1=F and 2=M) by a processor from statistics of the data itself. The distribution of variables, values, or variables and values, with or without other information and/or logic, is used to create a map from one semantic system to another. Similar distributions of other variable and/or values are likely to be for variables and/or values with the same meaning.
摘要翻译：提供医疗保健中语义的自动映射。数据集具有不同的语义（例如，在一个系统中用M和F指定的Gender，在另一个系统中用1或2指定的Sex）。对于语义互操作性，根据数据本身的统计，处理器创建不同医疗保健实体的语义系统之间的语义链接（例如，Gender = Sex和/或1 = F和2 = M）。使用或不使用其他信息和/或逻辑的变量，值或变量和值的分布用于创建从一个语义系统到另一个语义系统的映射。其他变量和/或值的类似分布可能是具有相同含义的变量和/或值。

30. 发明申请

US20140095201A1 Leveraging Public Health Data for Prediction and Prevention of Adverse Events 审中-公开
标题翻译：利用公共卫生数据预测和预防不良事件
公开(公告)号：US20140095201A1
公开(公告)日：2014-04-03
申请号：US14032522
申请日：2013-09-20
申请人： Faisal Farooq , Balaji Krishnapuram , Glenn Fung , Shipeng Yu , Karen Nielsen
发明人： Faisal Farooq , Balaji Krishnapuram , Glenn Fung , Shipeng Yu , Karen Nielsen
IPC分类号： G06F19/00
CPC分类号： G16H50/30
摘要： An adverse event may be prevented by predicting the probability of a given patient to have or undergo the adverse event. The ability to predict the probability of the adverse event may be enhanced when a model is derived from public health data to categorize and propose values for medical record fields. The probability alone may prevent the adverse event by educating the patient or medical professional. The probability may be predicted at any time, such as upon entry of information for the patient, periodic analysis, or at the time of admission. The probability may be used to generate a workflow action item to reduce the probability, to warn, to output appropriate instructions, and/or assist in avoiding adverse event. The probability may be specific to a hospital, physician group, or other medical entity, allowing prevention to focus on past adverse event causes for the given entity.
摘要翻译：可以通过预测给定患者具有或经历不良事件的可能性来防止不良事件。当模型从公共卫生数据导出以对医疗记录领域进行分类和建议值时，可以增强预测不良事件概率的能力。单靠概率可以通过教育患者或医疗专业人员来预防不良事件。可以随时预测概率，例如在输入患者信息，定期分析或入院时。概率可以用于生成工作流动作项目以降低概率，警告输出适当的指令和/或协助避免不利事件。医院，医师团体或其他医疗机构的概率可能是特定的，允许预防集中于给定实体的过去不良事件原因。

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