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1. 发明申请

WO2013036586A1 KL-DIVERGENCE KERNEL REGRESSION FOR NON-GAUSSIAN FINGERPRINT BASED LOCALIZATION 审中-公开
标题翻译：基于非GAUSSIAN指纹定位的KL-DIVERGENCE KERNEL REGRESSION
公开(公告)号：WO2013036586A1
公开(公告)日：2013-03-14
申请号：PCT/US2012/053889
申请日：2012-09-06
申请人： ALCATEL-LUCENT , MIROWSKI, Piotr , STECK, Harald , WHITING, Philip, A. , PALANIAPPAN, Ravishankar , MAC DONALD, William, Michael , HO, Tin Kam
发明人： MIROWSKI, Piotr , STECK, Harald , WHITING, Philip, A. , PALANIAPPAN, Ravishankar , MAC DONALD, William, Michael , HO, Tin Kam
IPC分类号： G01S5/02
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 1m 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，其可以在办公环境中实现高达约1m的精度。

2. 发明申请

WO2008137090A2 KNOWLEDGE-BASED PROLIFERATION SIGNATURES AND METHODS OF USE 审中-公开
标题翻译：基于知识的增殖标志和使用方法
公开(公告)号：WO2008137090A2
公开(公告)日：2008-11-13
申请号：PCT/US2008/005705
申请日：2008-05-02
申请人： SIEMENS MEDICAL SOLUTIONS USA, INC. , STARMANS, Maud H.W. , KRISHNAPURAM, Balaji , SEIGNEURIC, Renaud G. , STECK, Harald , NUYTEN, Dimitry S.A. , BUFFA, Francesca Meteora , HARRIS, Adrian Lewellyn , WOUTERS, Bradly G. , LAMBIN, Philippe , RAO, R. Bharat , KRISHNAN, Sriram
发明人： STARMANS, Maud H.W. , KRISHNAPURAM, Balaji , SEIGNEURIC, Renaud G. , STECK, Harald , NUYTEN, Dimitry S.A. , BUFFA, Francesca Meteora , HARRIS, Adrian Lewellyn , WOUTERS, Bradly G. , LAMBIN, Philippe , RAO, R. Bharat , KRISHNAN, Sriram
IPC分类号： C12Q1/68 , G06F19/00
CPC分类号： C12Q1/6886 , C12Q2600/106 , C12Q2600/118 , C12Q2600/158 , G06F19/18 , G06F19/20 , Y02A90/26
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。

3. 发明申请

WO2007123914A2 PROGNOSIS MODELING FROM ONE OR MORE SOURCES OF INFORMATION 审中-公开
标题翻译：来自一个或多个信息来源的预测建模
公开(公告)号：WO2007123914A2
公开(公告)日：2007-11-01
申请号：PCT/US2007/009386
申请日：2007-04-17
申请人： SIEMENS MEDICAL SOLUTIONS USA, INC. , STECK, Harald , KRISHNAN, Sriram , RAO, R. Bharat , LAMBIN, Phillippe , DEHING-OBERIJE, Cary , DE RUYSSCHER, Dirk
发明人： STECK, Harald , KRISHNAN, Sriram , RAO, R. Bharat , LAMBIN, Phillippe , DEHING-OBERIJE, Cary , DE RUYSSCHER, Dirk
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 (14) is developed from literature. The model (14) is determined (204) by reverse engineering the literature reported quantities. A relationship of a given variable to a treatment outcome is derived (204) from the literature. A processor (12, 102) 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 (16, 18) 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。预后模型（14）从文献开发。通过逆向工程化文献报道的数量来确定模型（14）（204）。给定变量与治疗结果的关系从文献中得出（204）。然后，处理器（12,102）可以为一个或多个变量使用单独的患者值来预测结果。通过结合文献驱动模型包括数据驱动模型（16,18）可以提高精度。

4. 发明申请

WO2007024617A2 MEDICAL ONTOLOGIES FOR COMPUTER ASSISTED CLINICAL DECISION SUPPORT 审中-公开
标题翻译：计算机辅助临床决策支持的医学现状
公开(公告)号：WO2007024617A2
公开(公告)日：2007-03-01
申请号：PCT/US2006/032112
申请日：2006-08-17
申请人： SIEMENS MEDICAL SOLUTIONS USA, INC. , PANDYA, Abhinay, Mahesh , ROSALES, Romer, E. , RAO, R., Bharat , STECK, Harald
发明人： PANDYA, Abhinay, Mahesh , ROSALES, Romer, E. , RAO, R., Bharat , STECK, Harald
IPC分类号： G06F19/00
CPC分类号： G16H50/20 , G06F19/00

5. 发明申请

WO2008150514A3 SYSTEM AND METHOD FOR MEDICAL PREDICTIVE MODELS USING LIKELIHOOD GAMBLE PRICING 审中-公开
公开(公告)号：WO2008150514A3
公开(公告)日：2008-12-11
申请号：PCT/US2008/006970
申请日：2008-06-03
申请人： SIEMENS MEDICAL SOLUTIONS USA, INC. , FUNG, Glenn , GIANG, Phan Hong , STECK, Harald , RAO, R. Bharat
发明人： FUNG, Glenn , GIANG, Phan Hong , STECK, Harald , RAO, R. Bharat
IPC分类号： G06F19/00
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 x ◊ of a medical patient whose survival probability is to be classified, calculating (71) a parameter vector (i) that maximizes a log-likelihood function of β over the set of survival data, (ii), wherein the log likelihood (ii) is a strictly concave function of β and is a function of the scalar xβ, calculating a weight (72) w ◊ for example x ◊ , calculating (73) an updated parameter vector β* that maximizes a function (iii), wherein data points (y ◊ , x ◊ , w ◊ ) augment set D, calculating (74) a fair log likelihood ratio λ f from (i) and β* using (iv), and mapping (75) the fair log likelihood ratio λ f to a fair price y f ◊ , wherein said fair price is a probability that class label y ◊ for example x ◊ has a value of 1.

6. 发明申请

WO2008150514A2 SYSTEM AND METHOD FOR MEDICAL PREDICTIVE MODELS USING LIKELIHOOD GAMBLE PRICING 审中-公开
标题翻译：使用LIKELIHOOD GAMBLE PRICING的医学预测模型的系统和方法
公开(公告)号：WO2008150514A2
公开(公告)日：2008-12-11
申请号：PCT/US2008006970
申请日：2008-06-03
申请人： SIEMENS MEDICAL SOLUTIONS , FUNG GLENN , GIANG PHAN HONG , STECK HARALD , RAO R BHARAT
发明人： FUNG GLENN , GIANG PHAN HONG , STECK HARALD , RAO R BHARAT
IPC分类号： G06F19/00
CPC分类号： G06K9/6278 , 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 ß, providing an example x? of a medical patient whose survival probability is to be classified, calculating (71) a parameter vector (i) that maximizes a log-likelihood function of ß over the set of survival data, (ii), wherein the log likelihood (ii) is a strictly concave function of ß and is a function of the scalar xß, calculating a weight (72) w? for example x?, calculating (73) an updated parameter vector ß* that maximizes a function (iii), wherein data points (y?, x?, w?) augment set D, calculating (74) a fair log likelihood ratio ?f from (i) and ß* using (iv), and mapping (75) the fair log likelihood ratio ?f to a fair price yf ?,
摘要翻译：一种用于预测医疗患者的存活率的方法包括提供多个医疗患者的生存数据的集合D，提供具有相关参数向量ß的回归模型，提供示例x？计算（71）在生存数据集上最大化ß的对数似然函数的参数向量（i），（ii）其中对数似然度（ii）为 ß的严格凹函数是标量x的函数，计算权重（72）w？例如x 1，计算（73）最大化函数（iii）的更新参数向量∈*，其中数据点（y 1，x 2，w 3）增加集合D，计算（74）公平对数似然比 f（i）和ß*使用（iv），并将公平对数似然比Δf映射（75）为公平价格yf？

7. 发明申请

WO2008137090A3 KNOWLEDGE-BASED PROLIFERATION SIGNATURES AND METHODS OF USE 审中-公开
标题翻译：基于知识的增殖标志和使用方法
公开(公告)号：WO2008137090A3
公开(公告)日：2009-02-26
申请号：PCT/US2008005705
申请日：2008-05-02
申请人： SIEMENS MEDICAL SOLUTIONS , STARMANS MAUD H W , KRISHNAPURAM BALAJI , SEIGNEURIC RENAUD G , STECK HARALD , NUYTEN DIMITRY S A , BUFFA FRANCESCA METEORA , HARRIS ADRIAN LEWELLYN , WOUTERS BRADLY G , LAMBIN PHILIPPE , RAO R BHARAT , KRISHNAN SRIRAM
发明人： STARMANS MAUD H W , KRISHNAPURAM BALAJI , SEIGNEURIC RENAUD G , STECK HARALD , NUYTEN DIMITRY S A , BUFFA FRANCESCA METEORA , HARRIS ADRIAN LEWELLYN , WOUTERS BRADLY G , LAMBIN PHILIPPE , RAO R BHARAT , KRISHNAN SRIRAM
IPC分类号： C12Q1/68 , G06F19/00
CPC分类号： C12Q1/6886 , C12Q2600/106 , C12Q2600/118 , C12Q2600/158 , G06F19/18 , G06F19/20 , Y02A90/26
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。

8. 发明申请

WO2007123914A3 PROGNOSIS MODELING FROM ONE OR MORE SOURCES OF INFORMATION 审中-公开
标题翻译：来自一个或多个信息来源的预测建模
公开(公告)号：WO2007123914A3
公开(公告)日：2008-01-24
申请号：PCT/US2007009386
申请日：2007-04-17
申请人： SIEMENS MEDICAL SOLUTIONS , STECK HARALD , KRISHNAN SRIRAM , RAO R BHARAT , LAMBIN PHILIPPE , DEHING-OBERIJE CARY , DE RUYSSCHER DIRK
发明人： STECK HARALD , KRISHNAN SRIRAM , RAO R BHARAT , LAMBIN PHILIPPE , DEHING-OBERIJE CARY , DE RUYSSCHER DIRK
IPC分类号： G06F19/00
CPC分类号： G16H50/20 , G16H50/70
摘要： A predictor of medical treatment outcome is developed and applied. A prognosis model (14) is developed from literature. The model (14) is determined (204) by reverse engineering the literature reported quantities. A relationship of a given variable to a treatment outcome is derived (204) from the literature. A processor (12, 102) 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 (16, 18) 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。预后模型（14）从文献开发。通过逆向工程化文献报道的数量来确定模型（14）（204）。给定变量与治疗结果的关系从文献中得出（204）。然后，处理器（12,102）可以为一个或多个变量使用单独的患者值来预测结果。通过结合文献驱动模型包括数据驱动模型（16,18）可以提高精度。

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