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1. 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。

2. 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。

3. 发明申请

US20080240425A1 Data De-Identification By Obfuscation 审中-公开
标题翻译：通过混淆的数据去识别
公开(公告)号：US20080240425A1
公开(公告)日：2008-10-02
申请号：US12048361
申请日：2008-03-14
申请人： Romer E. Rosales , Radu Stefan Niculescu , William A. Landi , Sriram Krishnan , R. Bharat Rao , Phan Hong Giang
发明人： Romer E. Rosales , Radu Stefan Niculescu , William A. Landi , Sriram Krishnan , R. Bharat Rao , Phan Hong Giang
IPC分类号： H04L9/28
CPC分类号： G06F21/6254
摘要： Medical or other data is de-identified by obfuscation. Located instances are replaced. By replacing with values in a same format and level of generality, multiple possible identifications—the replacement values and the instances not located—are provided in the data, obfuscating the original identification. By replacing as a function of a probability, the resulting data set has different instances distributed in a way making identification of the actual or original instances not located by searching more difficult.
摘要翻译：医疗或其他数据通过混淆来取消。定位的实例被替换。通过以相同格式和通用级别替换值，可以在数据中提供多个可能的标识 - 替换值和未定位的实例，从而模糊原始标识。通过将概率替换为函数，所得到的数据集具有不同的实例，其分布方式使得通过搜索更困难而不是定位的实际或原始实例的标识。

4. 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。

5. 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。

6. 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。

7. 发明授权

US08670997B2 Quality metric extraction and editing for medical data 有权
标题翻译：医疗数据质量量度提取和编辑
公开(公告)号：US08670997B2
公开(公告)日：2014-03-11
申请号：US11672639
申请日：2007-02-08
申请人： Sriram Krishnan , William A. Landi , Harald Steck , Romer E. Rosales , Radu Stefan Niculescu , Farbod Rahmanian , R. Bharat Rao
发明人： Sriram Krishnan , William A. Landi , Harald Steck , Romer E. Rosales , Radu Stefan Niculescu , Farbod Rahmanian , R. Bharat Rao
IPC分类号： G06F19/00 , G06Q50/00
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以进行报告。患者记录被开采。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。

8. 发明申请

US20110059074A1 Knowledge-Based Proliferation Signatures and Methods of Use 审中-公开
标题翻译：基于知识的增殖签名和使用方法
公开(公告)号：US20110059074A1
公开(公告)日：2011-03-10
申请号：US12597610
申请日：2008-05-02
申请人： Maud H.W. Starmans , Balaji Krishnapuram , Ranand G. Seigneuric , Harald Steck , Dimitry S.A. Nuyten , Francesca Buffa , Adrian Lewellyn Harris , Bradly G. Wouters , Philippe Lambin , R. Bharat Rao , Sriram Krishnan
发明人： Maud H.W. Starmans , Balaji Krishnapuram , Ranand G. Seigneuric , Harald Steck , Dimitry S.A. Nuyten , Francesca Buffa , Adrian Lewellyn Harris , Bradly G. Wouters , Philippe Lambin , R. Bharat Rao , Sriram Krishnan
IPC分类号： A61K39/395 , G01N33/48 , C12Q1/68 , C40B30/04 , C40B40/06 , A61K31/337 , A61K31/506 , A61K31/4045 , A61K33/24 , A61K31/7048 , A61K31/475 , A61K31/519 , A61K31/704 , A61K31/675 , A61K31/513 , A61K31/437 , A61K38/16 , A61P35/00
CPC分类号： C12Q1/6886 , C12Q2600/118 , C12Q2600/158 , G16B20/00 , G16B25/00 , Y10T436/143333
摘要： The present invention provides methods and compositions for predicting patient responses to cancer treatment using a proliferation gene signature. These methods can comprise measuring in a biological sample from a patient the levels of gene expression of a group of the genes designated herein. The present invention also provides for microarrays that can detect expression from a group of genes.
摘要翻译：本发明提供了使用增殖基因特征预测患者对癌症治疗的反应的方法和组合物。这些方法可以包括在来自患者的生物样品中测量本文指定的一组基因的基因表达水平。本发明还提供了可以检测一组基因的表达的微阵列。

9. 发明授权

US07840512B2 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：US07840512B2
公开(公告)日：2010-11-23
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： G06F17/00 , 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。

10. 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。

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