公开(公告)号：US20070280530A1

公开(公告)日：2007-12-06

申请号：US11742781

申请日：2007-05-01

申请人： Glenn Fung ,  Balaji Krishnapuram ,  Volkan Vural ,  R. Rao

发明人： Glenn Fung ,  Balaji Krishnapuram ,  Volkan Vural ,  R. Rao

IPC分类号： G06K9/62

CPC分类号： G06T7/0012 ,  G06K9/6269 ,  G06K9/6278

摘要： A method and system correlate candidate information and provide batch classification of a number of related candidates. The batch of candidates may be identified from a single data set. There may be internal correlations and/or differences among the candidates. The candidates may be classified taking into consideration the internal correlations and/or differences. The locations and descriptive features of a batch of candidates may be determined. In turn, the locations and/or descriptive features determined may used to enhance the accuracy of the classification of some or all of the candidates within the batch. In one embodiment, the single data set analyzed is associated with an internal image of patient and the distance between candidates is accounted for. Two different algorithms may each simultaneously classify all of the samples within a batch, one being based upon probabilistic analysis and the other upon a mathematical programming approach. Alternate algorithms may be used.

摘要翻译： 一种方法和系统将候选信息相关联并提供一些相关候选者的批次分类。 可以从单个数据集中识别该批候选。 候选人之间可能存在内部相关性和/或差异。 候选人可以考虑内部相关性和/或差异进行分类。 可以确定一批候选人的位置和描述性特征。 反过来，所确定的位置和/或描述性特征可以用于提高批次内的一些或所有候选者的分类的准确性。 在一个实施例中，所分析的单个数据集与患者的内部图像相关联，并且考虑候选者之间的距离。 两种不同的算法可以各自同时对批次中的所有样本进行分类，一种基于概率分析，另一种基于数学规划方法。 可以使用替代算法。

公开(公告)号：US07912278B2

公开(公告)日：2011-03-22

申请号：US11742781

申请日：2007-05-01

申请人： Glenn Fung ,  Balaji Krishnapuram ,  Volkan Vural ,  R. Bharat Rao

发明人： Glenn Fung ,  Balaji Krishnapuram ,  Volkan Vural ,  R. Bharat Rao

IPC分类号： G06K9/46 ,  G06K9/62

CPC分类号： G06T7/0012 ,  G06K9/6269 ,  G06K9/6278

摘要： A method and system correlate candidate information and provide batch classification of a number of related candidates. The batch of candidates may be identified from a single data set. There may be internal correlations and/or differences among the candidates. The candidates may be classified taking into consideration the internal correlations and/or differences. The locations and descriptive features of a batch of candidates may be determined. In turn, the locations and/or descriptive features determined may used to enhance the accuracy of the classification of some or all of the candidates within the batch. In one embodiment, the single data set analyzed is associated with an internal image of patient and the distance between candidates is accounted for. Two different algorithms may each simultaneously classify all of the samples within a batch, one being based upon probabilistic analysis and the other upon a mathematical programming approach. Alternate algorithms may be used.

摘要翻译： 一种方法和系统将候选信息相关联并提供一些相关候选者的批次分类。 可以从单个数据集中识别该批候选。 候选人之间可能存在内部相关性和/或差异。 候选人可以考虑内部相关性和/或差异进行分类。 可以确定一批候选人的位置和描述性特征。 反过来，所确定的位置和/或描述性特征可以用于提高批次内的一些或所有候选者的分类的准确性。 在一个实施例中，所分析的单个数据集与患者的内部图像相关联，并且考虑候选者之间的距离。 两种不同的算法可以各自同时对批次中的所有样本进行分类，一种基于概率分析，另一种基于数学规划方法。 可以使用替代算法。

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。

公开(公告)号：US20140095204A1

公开(公告)日：2014-04-03

申请号：US14037469

申请日：2013-09-26

申请人： Glenn Fung ,  Balaji Krishnapuram ,  Faisal Farooq ,  Shipeng Yu ,  Bharat R. Rao ,  Vikram Anand

发明人： Glenn Fung ,  Balaji Krishnapuram ,  Faisal Farooq ,  Shipeng Yu ,  Bharat R. Rao ,  Vikram Anand

IPC分类号： G06F19/00

CPC分类号： G16H50/20 ,  G16H50/70

摘要： Inclusion of a patient in a medical category is determined by triggering an analysis of an electronic medical record of the patient in response to an input of data into the electronic medical record. Identifying characteristics that indicate inclusion in the medical category with the analysis, and determining a probability the patient belongs to the medical category based on the identified characteristics.

摘要翻译： 通过响应于将数据输入到电子医疗记录中的触发对患者的电子医疗记录的分析来确定医疗类别中的患者的包含。 通过分析来识别指示包含在医疗类别中的特征，并且基于所识别的特征来确定患者属于医疗类别的概率。

公开(公告)号：US20140088989A1

公开(公告)日：2014-03-27

申请号：US14027494

申请日：2013-09-16

申请人： Balaji Krishnapuram ,  Bharat R. Rao ,  Glenn Fung ,  Vikram Anand ,  Faisal Farooq ,  Wolfgang Wiessler ,  Shipeng Yu

发明人： Balaji Krishnapuram ,  Bharat R. Rao ,  Glenn Fung ,  Vikram Anand ,  Faisal Farooq ,  Wolfgang Wiessler ,  Shipeng Yu

IPC分类号： G06F19/00

CPC分类号： G16H50/70 ,  G16H50/50

摘要： A predictive model of medical knowledge is trained from patient data of multiple different medical centers. The predictive model is machine learnt from routine patient data from multiple medical centers. Distributed learning avoids transfer of the patient data from any of the medical centers. Each medical center trains the predictive model from the local patient data. The learned statistics, and not patient data, are transmitted to a central server. The central server reconciles the statistics and proposes new statistics to each of the local medical centers. In an iterative approach, the predictive model is developed without transfer of patient data but with statistics responsive to patient data available from multiple medical centers. To assure comfort with the process, the transmitted statistics may be in a human readable format.

摘要翻译： 医学知识的预测模型是从多个不同医疗中心的患者数据进行培训。 预测模型是从多个医疗中心的常规患者数据获得的机器。 分布式学习避免了从任何医疗中心转移患者数据。 每个医疗中心从当地患者数据中训练预测模型。 学习的统计信息而不是患者数据被传送到中央服务器。 中央服务器统计统计数据，并向每个当地医疗中心提出新的统计数据。 在迭代方法中，预测模型是在没有转移患者数据的情况下开发的，但是对于可从多个医疗中心获得的患者数据进行统计。 为了确保该过程的舒适度，所发送的统计数据可以是人类可读的格式。

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.

公开(公告)号：US20070189602A1

公开(公告)日：2007-08-16

申请号：US11671777

申请日：2007-02-06

申请人： R. Rao ,  Murat Dundar ,  Balaji Krishnapuram ,  Glenn Fung

发明人： R. Rao ,  Murat Dundar ,  Balaji Krishnapuram ,  Glenn Fung

IPC分类号： G06K9/62 ,  G06K9/00

CPC分类号： G06K9/6277 ,  G06T7/0012 ,  G06T2207/30004

摘要： A method of training a classifier for computer aided detection of digitized medical images, includes providing a plurality of bags, each bag containing a plurality of feature samples of a single region-of-interest in a medical image, wherein said features include texture, shape, intensity, and contrast of said region-of-interest, wherein each region-of-interest has been labeled as either malignant or healthy, and training a classifier on said plurality of bags of feature samples, subject to the constraint that at least one point in a convex hull of each bag, corresponding to a feature sample, is correctly classified according to the labeled of the associated region-of-interest.

摘要翻译： 一种训练用于数字化医学图像的计算机辅助检测的分类器的方法，包括提供多个袋，每个袋包含在医学图像中的单个感兴趣区域的多个特征样本，其中所述特征包括纹理，形状 所述感兴趣区域的强度和对比度，其中每个感兴趣区域已被标记为恶性或健康的，并且在所述多个特征样本袋上训练分类器，受限于至少一个 对应于特征样本的每个袋的凸包中的点被根据相关联的感兴趣区域的标记进行正确分类。

公开(公告)号：US08751495B2

公开(公告)日：2014-06-10

申请号：US12891983

申请日：2010-09-28

申请人： Stanley Chung ,  Faisal Farooq ,  Glenn Fung ,  Balaji Krishnapuram ,  R. Bharat Rao ,  Romer E. Rosales ,  John Weis ,  Shipeng Yu

发明人： Stanley Chung ,  Faisal Farooq ,  Glenn Fung ,  Balaji Krishnapuram ,  R. Bharat Rao ,  Romer E. Rosales ,  John Weis ,  Shipeng Yu

IPC分类号： G06F7/00 ,  G06F17/00 ,  G06F17/30

CPC分类号： G06F17/30616 ,  G06F19/00 ,  G16H50/70

摘要： A method, including receiving a data source selection from a user or software application, the data source including medical information of a plurality of patients, receiving, from the user or software application, a data pattern that is related to a concept to be explored in the data source, querying the data source to find information that approximately matches the data pattern; and receiving the information from the data source, wherein the information includes unstructured data, assigning a classification to individual parts of the information based on the part's relationship to the data pattern, and outputting the classified information to the user or software application.

摘要翻译： 一种方法，包括从用户或软件应用接收数据源选择，所述数据源包括多个患者的医学信息，从用户或软件应用程序接收与将要探索的概念相关的数据模式 数据源，查询数据源以查找与数据模式近似匹配的信息; 以及从所述数据源接收所述信息，其中，所述信息包括非结构化数据，基于所述部分与所述数据模式的关系，将所述信息的各个部分分配给所述信息，并将所述分类信息输出到所述用户或软件应用。

公开(公告)号：US20110078145A1

公开(公告)日：2011-03-31

申请号：US12891983

申请日：2010-09-28

申请人： Stanley Chung ,  Faisal Farooq ,  Glenn Fung ,  Balaji Krishnapuram ,  R. Bharat Rao ,  Romer E. Rosales ,  John Weis ,  Shipeng Yu

发明人： Stanley Chung ,  Faisal Farooq ,  Glenn Fung ,  Balaji Krishnapuram ,  R. Bharat Rao ,  Romer E. Rosales ,  John Weis ,  Shipeng Yu

IPC分类号： G06F17/30 ,  G06F3/048

CPC分类号： G06F17/30616 ,  G06F19/00 ,  G16H50/70

摘要： A method, including receiving a data source selection from a user or software application, the data source including medical information of a plurality of patients, receiving, from the user or software application, a data pattern that is related to a concept to be explored in the data source, querying the data source to find information that approximately matches the data pattern; and receiving the information from the data source, wherein the information includes unstructured data, assigning a classification to individual parts of the information based on the part's relationship to the data pattern, and outputting the classified information to the user or software application.

摘要翻译： 一种方法，包括从用户或软件应用接收数据源选择，所述数据源包括多个患者的医学信息，从用户或软件应用程序接收与将要探索的概念相关的数据模式 数据源，查询数据源以查找与数据模式近似匹配的信息; 以及从所述数据源接收所述信息，其中，所述信息包括非结构化数据，基于所述部分与所述数据模式的关系，将所述信息的各个部分分配给所述信息，并将所述分类信息输出到所述用户或软件应用。

公开(公告)号：US07986827B2

公开(公告)日：2011-07-26

申请号：US11671777

申请日：2007-02-06

申请人： R. Bharat Rao ,  Murat Dundar ,  Balaji Krishnapuram ,  Glenn Fung

发明人： R. Bharat Rao ,  Murat Dundar ,  Balaji Krishnapuram ,  Glenn Fung

IPC分类号： G06K9/62 ,  G06K9/00 ,  G06E1/00

CPC分类号： G06K9/6277 ,  G06T7/0012 ,  G06T2207/30004

摘要： A method of training a classifier for computer aided detection of digitized medical image, includes providing a plurality of bags, each bag containing a plurality of feature samples of a single region-of-interest in a medical image, where each region-of-interest has been labeled as either malignant or healthy. The training uses candidates that are spatially adjacent to each other, modeled by a “bag”, rather than each candidate by itself. A classifier is trained on the plurality of bags of feature samples, subject to the constraint that at least one point in a convex hull of each bag, corresponding to a feature sample, is correctly classified according to the label of the associated region-of-interest, rather than a large set of discrete constraints where at least one instance in each bag has to be correctly classified.

摘要翻译： 训练用于数字化医学图像的计算机辅助检测的分类器的方法包括提供多个袋，每个袋包含在医学图像中的单个感兴趣区域的多个特征样本，其中每个感兴趣的区域 已被标记为恶性或健康。 培训使用空间上相邻的候选人，由“包”建模，而不是每个候选人本身。 在多个特征样本袋上训练分类器，受限于根据相关区域的标签对每个袋子的凸包中的至少一个点（对应于特征样本）进行正确分类， 而不是大量离散约束，每个行李中的至少一个实例必须被正确分类。