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51. 发明授权

US07457731B2 Early detection of disease outbreak using electronic patient data to reduce public health threat from bio-terrorism 有权
标题翻译：使用电子患者数据早期发现疾病爆发，以减少生物恐怖主义对公共卫生的威胁
公开(公告)号：US07457731B2
公开(公告)日：2008-11-25
申请号：US10319365
申请日：2002-12-13
申请人： R. Bharat Rao
发明人： R. Bharat Rao
IPC分类号： G06F17/10
CPC分类号： G06Q50/24 , G06F19/00 , G06Q30/0202 , G06Q50/22 , G16H50/80 , Y02A90/22 , Y02A90/24 , Y02A90/26 , Y10S707/99939 , Y10S707/99945
摘要： A method is provided for automatically identifying a disease outbreak indicative of a potential bio-terror attack. Patient records are mined from structured and unstructured clinical sources. The patient records are then analyzed by correlating selected patient data contained in the patient records with disease indicia for each of a plurality of diseases. A probability of a disease outbreak is estimated at least in part based on these correlations. Suspicion may also be indicated if anomalous disease clusters are found. If any of the estimated probabilities exceeds a threshold value, a disease outbreak alert is outputted. The disease indicia may be defined by disease progression models, which may be stored in a disease knowledge base.
摘要翻译：提供了一种自动识别指示潜在的生物恐怖袭击的疾病暴发的方法。患者记录从结构化和非结构化临床来源开采。然后通过将包含在患者记录中的所选患者数据与多种疾病中的每一种的疾病标记相关联来分析患者记录。估计疾病爆发的概率至少部分地基于这些相关性。如果发现异常疾病群，也可能会显示怀疑。如果任一估计概率超过阈值，则输出疾病爆发警报。疾病标记可以由可能存储在疾病知识库中的疾病进展模型来定义。

52. 发明申请

US20080154897A1 Automated Interpretation and Replacement of Date References in Unstructured Text 审中-公开
标题翻译：在非结构化文本中自动解释和替换日期参考
公开(公告)号：US20080154897A1
公开(公告)日：2008-06-26
申请号：US11942127
申请日：2007-11-19
申请人： Yetisgen Yildiz Meliha , Radu Stefan Niculescu , Romer E. Rosales , R. Bharat Rao , Sriram Krishnan
发明人： Yetisgen Yildiz Meliha , Radu Stefan Niculescu , Romer E. Rosales , R. Bharat Rao , Sriram Krishnan
IPC分类号： G06F17/30
CPC分类号： G06F17/277 , G06F16/258
摘要： A method for interpreting date information from unstructured text includes performing phrase tokenization on the unstructured text to identify one or more temporal phrases. Word categorization is performed on the one or more temporal phrases to categorize one or more words of each temporal phrase. Grammar analysis is performed to match each temporal phrase to an understood syntax using the categorizations of the words of each temporal phrase. Each temporal phrase is interpreted based on the matched syntax.
摘要翻译：用于从非结构化文本解释日期信息的方法包括在非结构化文本上执行短语标记化以识别一个或多个时间短语。对一个或多个时间短语执行词分类以对每个时间短语的一个或多个单词进行分类。执行语法分析以使用每个时间短语的单词的分类将每个时间短语与理解的语法相匹配。基于匹配的语法来解释每个时间短语。

53. 发明授权

US08652039B2 Guiding differential diagnosis through information maximization 有权
标题翻译：通过信息最大化指导鉴别诊断
公开(公告)号：US08652039B2
公开(公告)日：2014-02-18
申请号：US11365271
申请日：2006-03-01
申请人： Romer E. Rosales , Marianne Larissa Mueller , Sriram Krishnan , R. Bharat Rao
发明人： Romer E. Rosales , Marianne Larissa Mueller , Sriram Krishnan , R. Bharat Rao
IPC分类号： A61B5/00 , G06F19/00
CPC分类号： G06F19/34 , G06F19/00 , G06F19/3481 , G06Q50/22 , G06Q50/24 , G16H15/00 , G16H50/20 , G16H50/70
摘要： A system and method for diagnosis and treatment decisions based on information maximization is disclosed. Utilizing patient information as well as clinical records from other patients can reduce the uncertainty in both diagnosis and treatment options. The information maximization may consider additional data such as risk, cost, and comfort in making a proper medical decision.
摘要翻译：公开了一种基于信息最大化的诊断和处理决策的系统和方法。利用患者信息以及其他患者的临床记录，可以减少诊断和治疗方案的不确定性。信息最大化可以考虑额外的数据，如风险，成本和舒适度，做出适当的医疗决策。

54. 发明授权

US08250013B2 System and method for privacy preserving predictive models for lung cancer survival analysis 有权
标题翻译：用于隐私保护肺癌存活分析预测模型的系统和方法
公开(公告)号：US08250013B2
公开(公告)日：2012-08-21
申请号：US12353310
申请日：2009-01-14
申请人： Glenn Fung , R. Bharat Rao , Sriram Krishnan , Shipeng Yu , Cary Dehing-Oberije , Philippe Lambin , Dirk de Ruysscher
发明人： Glenn Fung , R. Bharat Rao , Sriram Krishnan , Shipeng Yu , Cary Dehing-Oberije , Philippe Lambin , Dirk de Ruysscher
IPC分类号： G06F17/00 , G06N5/00
CPC分类号： G06K9/62 , G06F19/00 , G06F21/6254 , G06K9/6215 , G06K9/6232 , G06K9/6269 , G16H50/20 , G16H50/70
摘要： A computer-implemented method for privacy-preserving data mining to determine cancer survival rates includes providing a random matrix B agreed to by a plurality of entities, wherein each entity i possesses a data matrix Ai of cancer survival data that is not publicly available, providing a class matrix Di for each of the data matrices Ai, providing a kernel K(Ai, B) by each of said plurality of entities to allow public computation of a full kernel, and computing a binary classifier that incorporates said public full kernel, wherein said classifier is adapted to classify a new data vector according to a sign of said classifier.
摘要翻译：用于隐私保护数据挖掘以确定癌症存活率的计算机实现的方法包括提供由多个实体同意的随机矩阵B，其中每个实体拥有不可公开获得的癌症存活数据的数据矩阵Ai，提供用于每个数据矩阵Ai的类矩阵Di，由所述多个实体中的每一个提供内核K（Ai，B），以允许公共计算完整内核，以及计算并入所述公共完整内核的二进制分类器，其中所述分类器适于根据所述分类器的符号对新数据向量进行分类。

55. 发明授权

US07965869B2 System and method for detecting and matching anatomical structures using appearance and shape 有权
标题翻译：使用外观和形状检测和匹配解剖结构的系统和方法
公开(公告)号：US07965869B2
公开(公告)日：2011-06-21
申请号：US12401909
申请日：2009-03-11
申请人： Xiang Zhou , Bogdan Georgescu , Dorin Comaniciu , R. Bharat Rao , Alok Gupta
发明人： Xiang Zhou , Bogdan Georgescu , Dorin Comaniciu , R. Bharat Rao , Alok Gupta
IPC分类号： G06K9/00 , G06K9/46 , G06K9/36
CPC分类号： G06K9/6206 , G06K9/6256 , G06T7/12 , G06T7/149 , G06T7/251 , G06T2207/10132 , G06T2207/30048
摘要： A detection framework that matches anatomical structures using appearance and shape is disclosed. A training set of images are used in which object shapes or structures are annotated in the images. A second training set of images represents negative examples for such shapes and structures, i.e., images containing no such objects or structures. A classification algorithm trained on the training sets is used to detect a structure at its location. The structure is matched to a counterpart in the training set that can provide details about the structure's shape and appearance.
摘要翻译：公开了一种使用外观和形状匹配解剖结构的检测框架。使用图像的训练集，其中在图像中注释对象形状或结构。图像的第二训练集表示这种形状和结构的负面例子，即不包含这样的对象或结构的图像。训练集训练的分类算法用于检测其位置的结构。该结构与训练集中的对应物匹配，可以提供关于结构形状和外观的细节。

56. 发明授权

US07877272B2 Computer instructions for guiding differential diagnosis through information maximization 有权
标题翻译：通过信息最大化指导鉴别诊断的计算机指令
公开(公告)号：US07877272B2
公开(公告)日：2011-01-25
申请号：US12038169
申请日：2008-02-27
申请人： Romer E. Rosales , Marianne Larissa Mueller , Sriram Krishnan , R. Bharat Rao
发明人： Romer E. Rosales , Marianne Larissa Mueller , Sriram Krishnan , R. Bharat Rao
IPC分类号： G06Q10/00
CPC分类号： G06F19/34 , G06F19/00 , G06F19/3481 , G06Q50/22 , G06Q50/24 , G16H15/00 , G16H50/20 , G16H50/70
摘要： A system and method for diagnosis and treatment decisions based on information maximization is disclosed. Utilizing patient information as well as clinical records from other patients can reduce the uncertainty in both diagnosis and treatment options. The information maximization may consider additional data such as risk, cost, and comfort in making a proper medical decision.
摘要翻译：公开了一种基于信息最大化的诊断和处理决策的系统和方法。利用患者信息以及其他患者的临床记录，可以减少诊断和治疗方案的不确定性。信息最大化可以考虑额外的数据，如风险，成本和舒适度，做出适当的医疗决策。

57. 发明授权

US07840511B2 Learning or inferring medical concepts from medical transcripts using probabilistic models with words or phrases identification 有权
标题翻译：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。

58. 发明授权

US07650321B2 Two classifier based system for classifying anomalous medical patient records 有权
标题翻译：两种基于分类器的系统，用于对异常医疗病人记录进行分类
公开(公告)号：US07650321B2
公开(公告)日：2010-01-19
申请号：US11355299
申请日：2006-02-15
申请人： Sriram Krishnan , R. Bharat Rao
发明人： Sriram Krishnan , R. Bharat Rao
IPC分类号： G06N5/04 , G06E1/00
CPC分类号： G16H50/20 , G06F19/00 , G16H10/60
摘要： Missing data is addressed in a medical decision support system. The classifier applied to the patient record with missing data is obtained as a function of the available data. For example, one of a plurality of different classifiers is selected based on the features available in the patient record to be classified. The different classifiers are developed using different feature sets. The classifier developed using a feature set closest to or a sub-set of the features available in the patient record is selected for classifying the patient record. As another example, features in a training set corresponding to features available in the patient record are used to build a classifier. The classifier is applied to the patient record by inputting the available features of the patient record.
摘要翻译：缺失的数据在医疗决策支持系统中得到解决。作为可用数据的函数获得应用于具有缺失数据的病人记录的分类器。例如，基于要分类的患者记录中可用的特征来选择多个不同分类器之一。不同的分类器是使用不同的特征集开发的。选择使用最接近或者患者记录中可用特征的子集的特征集开发的分类器用于对患者记录进行分类。作为另一示例，使用对应于患者记录中可用特征的训练集中的特征来建立分类器。通过输入患者记录的可用特征将分类器应用于患者记录。

59. 发明授权

US07640051B2 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：US07640051B2
公开(公告)日：2009-12-29
申请号：US10877129
申请日：2004-06-25
申请人： Sriram Krishnan , R. Bharat Rao , Murat Dundar , Glenn Fung
发明人： Sriram Krishnan , R. Bharat Rao , Murat Dundar , Glenn Fung
IPC分类号： A61B5/00
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。

60. 发明申请

US20090234628A1 PREDICTION OF COMPLETE RESPONSE GIVEN TREATMENT DATA 审中-公开
标题翻译：预测完全响应治疗数据
公开(公告)号：US20090234628A1
公开(公告)日：2009-09-17
申请号：US12400868
申请日：2009-03-10
申请人： Shipeng Yu , Glenn Fung , Cary Dehing-Oberije , Lucas Carolus Gertrudis Gerardus Persoon , Sriram Krishnan , R. Bharat Rao , Philippe Lambin , Ruud G.P.M. Van Stiphout , Jeroen Buijsen , Guido Lammering , Marco Janssen , Eric Postma , Vincenzo Valentini
发明人： Shipeng Yu , Glenn Fung , Cary Dehing-Oberije , Lucas Carolus Gertrudis Gerardus Persoon , Sriram Krishnan , R. Bharat Rao , Philippe Lambin , Ruud G.P.M. Van Stiphout , Jeroen Buijsen , Guido Lammering , Marco Janssen , Eric Postma , Vincenzo Valentini
IPC分类号： G06G7/48 , G06F15/18
CPC分类号： A61N5/1031 , A61N2005/1041 , G06N20/00 , G16H50/50
摘要： A system for modeling complete response prediction is provided. The system includes an input that is operable to receive treatment information representing treatment data that may be used to predict a complete response of a tumor. The complete response may include a disappearance of all or substantially all of a disease. A processor may be operable to use a model to predict complete response of the tumor as a function of the treatment data. The model represents a probability of complete response to treatment given the treatment data. A display is operable to output an image as a function of the complete response prediction.
摘要翻译：提供了一种完整的响应预测建模系统。该系统包括可操作以接收表示可用于预测肿瘤的完整反应的治疗数据的治疗信息的输入。完整的反应可能包括所有或基本上所有疾病的消失。处理器可操作以使用模型来预测作为治疗数据的函数的肿瘤的完全反应。该模型表示给予治疗数据的治疗完全反应的概率。显示器可操作以输出作为完整响应预测的函数的图像。

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