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1. 发明申请

WO2009091595A2 SYSTEM AND METHOD FOR PRIVACY PRESERVING PREDICTIVE MODELS FOR LUNG CANCER SURVIVAL ANALYSIS 审中-公开
标题翻译：用于肺癌预防分析的预防性预防模型的隐私保护系统和方法
公开(公告)号：WO2009091595A2
公开(公告)日：2009-07-23
申请号：PCT/US2009/000313
申请日：2009-01-16
申请人： SIEMENS MEDICAL SOLUTIONS USA, INC. , MAASTRO CLINIC , FUNG, Glenn , RAO, R. Bharat , KRISHNAN, Sriram , YU, Shipeng , DEHING-OBERIJE, Cary , LAMBIN, Phillippe , DE RUYSSCHER, Dirk
发明人： FUNG, Glenn , RAO, R. Bharat , KRISHNAN, Sriram , YU, Shipeng , DEHING-OBERIJE, Cary , LAMBIN, Phillippe , DE RUYSSCHER, Dirk
IPC分类号： G06K9/62
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 (21) a random matrix B agreed to by a plurality of entities, wherein each entity i possesses a data matrix A i of cancer survival data that is not publicly available, providing (22) a class matrix D i for each of the data matrices A i , providing (23) a kernel K(A i , B) by each of said plurality of entities to allow public computation of a full kernel, and computing a(24) binary classifier that incorporates said public full kernel, wherein said classifier is adapted to classify (25) 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方法包括提供（21）由多个实体同意的随机矩阵B，其中每个实体i拥有不是的癌症存活数据的数据矩阵A i 为每个数据矩阵A i提供（22）类矩阵D i，提供（23）由所述多个实体中的每一个实体的核K（A i，B），以允许公共计算完整内核，以及计算包含所述公共完整内核的（24）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（25）新的数据向量进行分类。

2. 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。

3. 发明申请

WO2008156716A1 AUTOMATED REDUCTION OF BIOMARKERS 审中-公开
标题翻译：自动减少生物标志物
公开(公告)号：WO2008156716A1
公开(公告)日：2008-12-24
申请号：PCT/US2008/007468
申请日：2008-06-16
申请人： SIEMENS MEDICAL SOLUTIONS USA, INC. , FUNG, Glenn , SEIGNEURIC, Renaud , KRISHNAN, Sriram , RAO, R. Bharat , LAMBIN, Philippe
发明人： FUNG, Glenn , SEIGNEURIC, Renaud , KRISHNAN, Sriram , RAO, R. Bharat , LAMBIN, Philippe
IPC分类号： G06F19/00
CPC分类号： G06F19/20 , G06F19/24
摘要： A list of biomarkers indicative of patient outcome is reduced. A computer program is applied to a set of biomarkers indicative of a patient outcome (e.g., prognosis, diagnosis, or treatment result). The computer program models the set of biomarkers with a subset of the biomarkers. The subset is identified without labeling based on the patient outcome. Instead, biomarker scores (e.g., sequence score) are used to identify the subset of biomarkers.
摘要翻译：减少了指示患者结果的生物标志物的列表。将计算机程序应用于指示患者结果的一组生物标志物（例如，预后，诊断或治疗结果）。计算机程序用生物标志物的一个子集建模该组生物标志物。基于患者结果，该子集被识别而没有标记。相反，生物标志物评分（例如，序列评分）用于鉴定生物标志物的子集。

4. 发明申请

WO2006132975A1 SYSTEM AND METHOD FOR LEARNING RANKINGS VIA CONVEX HULL SEPARATION 审中-公开
标题翻译：通过凸轮分离学习排名的系统和方法
公开(公告)号：WO2006132975A1
公开(公告)日：2006-12-14
申请号：PCT/US2006/021475
申请日：2006-06-02
申请人： SIEMENS MEDICAL SOLUTIONS USA, INC. , BI, Jinbo , FUNG, Glenn , KRISHNAN, Sriram , KRISHNAPURAM, Balaji , RAO, R. Bharat , ROSALES, Romer, E.
发明人： BI, Jinbo , FUNG, Glenn , KRISHNAN, Sriram , KRISHNAPURAM, Balaji , RAO, R. Bharat , ROSALES, Romer, E.
IPC分类号： G06K9/00
CPC分类号： G06K9/6269
摘要： A method for finding a ranking function /that classifies feature points in an n- dimensional space includes providing (61) a plurality of feature points Xk derived from tissue sample regions in a digital medical image, providing (62) training data A comprising training samples A j where formula (I), providing (63) an ordering E = {( P,Q )| A P A Q } of at least some training data sets where all training samples x I ∈ A p are ranked higher than any sample x j ∈ A Q , and solving (64) a mathematical optimization program to determine the ranking function f that classifies said feature points x into sets A . For any two sets A i , A j , A i - A j , and the ranking function f satisfies inequality constraints f ( x i ) ≤ f ( x j ) for all x I ∈ conv(A i ) and x j ∈ conv (A j ) , where conv ( A ) represents the convex hull of the elements of set A .
摘要翻译：一种用于寻找在第n维空间中对特征点进行分类的分级功能的方法包括提供（61）从数字医学图像中的组织样本区域导出的多个特征点X k，提供（62）包括训练样本的训练数据A 一个

5. 发明申请

WO2006083569A1 CHARACTERIZATION OF CARDIAC MOTION WITH SPATIAL RELATIONSHIP 审中-公开
标题翻译：心理运动与空间关系的表征
公开(公告)号：WO2006083569A1
公开(公告)日：2006-08-10
申请号：PCT/US2006/001931
申请日：2006-01-18
申请人： SIEMENS MEDICAL SOLUTIONS USA, INC. , QAZI, Maleeha , KAMASAK, Mustafa , DUNDAR, Murat , FUNG, Glenn , KRISHNAN, Sriram , RAO, R. Bharat
发明人： QAZI, Maleeha , KAMASAK, Mustafa , DUNDAR, Murat , FUNG, Glenn , KRISHNAN, Sriram , RAO, R. Bharat
IPC分类号： A61B5/00
CPC分类号： G06T7/0012 , A61B5/00 , A61B5/055 , A61B5/7267 , A61B6/03 , A61B6/5217 , A61B8/08 , G06F19/00 , G06F19/321 , G06K9/00335 , G06K2209/051 , G06T7/20 , G06T2207/30048 , G16H50/20 , G16H50/50
摘要： Cardiac motion is automatically characterized (28) based on spatial relationship to health. A classifier is trained for the characterization of cardiac motion. Regional wall motion abnormality assessment (28) may be improved by combining information from neighboring segments (S). The structure or relationship between different segments (S) and associated probabilities of different spatial locations being abnormal given another segment (S) being abnormal are used for classification.
摘要翻译：基于与健康的空间关系，心脏运动被自动表征（28）。训练分类器用于表征心脏运动。可以通过组合来自相邻段（S）的信息来改善局部壁运动异常评估（28）。用于分类的另一段（S）异常的不同段（S）和不同空间位置的相关概率之间的结构或关系异常。

6. 发明申请

WO2008143865A1 SYSTEM AND METHOD FOR LARGE SCALE CODE CLASSIFICATION FOR MEDICAL PATIENT RECORDS 审中-公开
标题翻译：用于医疗患者记录的大规模代码分类的系统和方法
公开(公告)号：WO2008143865A1
公开(公告)日：2008-11-27
申请号：PCT/US2008/006141
申请日：2008-05-14
申请人： SIEMENS MEDICAL SOLUTIONS USA, INC. , NICULESCU, Radu Stefan , RAO, R. Bharat , LITA, Lucian Vlad , YU, Shipeng
发明人： NICULESCU, Radu Stefan , RAO, R. Bharat , LITA, Lucian Vlad , YU, Shipeng
IPC分类号： G06F19/00
CPC分类号： G06Q50/24 , G06F19/00 , G16H50/20
摘要： A method for training classifiers for ICD-9 patient codes includes providing (10) a set of documents regarding patient hospital visits, combining (11) the documents for each patient visit to create a hospital visit profile, defining (12) a feature as an ngram with a frequency of occurrence greater or equal to a predetermined value that does not appear in a standard list of ngrams, processing (13) the profiles to remove redundancy at a paragraph level and perform tokenization and sentence splitting, performing (14) feature selection, randomly (15) dividing the documents into training, validation, and test sets, and training (16, 17, 18) a set of binary classifiers using a weighted ridge regression, each binary classifier targeting a single ICD-9 code using the training set, wherein each classifier is adapted to determining a specific ICD-9 code by analyzing a patient's hospital records. Figures 1a-b
摘要翻译：用于训练ICD-9患者代码分类器的方法包括提供（10）关于患者医院访问的一组文件，组合（11）每个病人访问的文档以创建医院访问简档，将（12）特征定义为 ngram的发生频率大于或等于不出现在标准列表中的预定值，处理（13）删除段落级别的冗余并执行标记化和句子分割的简档，执行（14）特征选择随机（15）将文档划分为训练，验证和测试集，并使用加权脊回归训练（16,17,18）一组二进制分类器，每个二进制分类器使用训练针对单个ICD-9代码其中每个分类器适于通过分析患者的医院记录来确定特定的ICD-9代码。图1a-b

7. 发明申请

WO2006125145A2 PATIENT DATA MINING IMPROVEMENTS 审中-公开
标题翻译：患者数据挖掘改进
公开(公告)号：WO2006125145A2
公开(公告)日：2006-11-23
申请号：PCT/US2006/019418
申请日：2006-05-18
申请人： SIEMENS MEDICAL SOLUTIONS USA, INC. , RAO, R. Bharat , KRISHNAN, Sriram , LANDI, William A. , CASE, Randall
发明人： RAO, R. Bharat , KRISHNAN, Sriram , LANDI, William A. , CASE, Randall
CPC分类号： G06F19/321 , G06F19/00 , G06Q50/24 , G16H50/70
摘要： Improvements in mining information (402) from patient records and/or use (404) of such mined information are provided. A scheduled appointment (406) is identified. In response, the patient record is mined (402) to identify any possible lack of adherence. A form (408) requiring authorization and including a clinical action or prescription is generated to address, at least in part, the lack of adherence. Contact (410) with a patient is initiated by a processor (102), at least in part, in response to a lack of adherence. A request for documentation (412) is generated in response to mining indicating an inadequate probability of adherence to a guideline. During real-time, additional information to be obtained which may alter a probability is suggested.
摘要翻译：提供了来自患者记录和/或使用（404）这种开采信息的挖掘信息（402）的改进。确定了预定的约会（406）。作为回应，患者记录被开采（402）以识别任何可能的不遵守。产生要求授权并包括临床行为或处方的表格（408），至少部分地解决缺乏遵守性。与患者的接触（410）由处理器（102）发起，至少部分地响应于缺乏依从性。响应于挖掘而产生文档请求（412），表明遵守准则的概率不足。建议实时更新可能会改变概率的信息。

8. 发明申请

WO2005078631A1 HIERARCHICAL MODELING IN MEDICAL ABNORMALITY DETECTION 审中-公开
标题翻译：医学异常检测中的分层建模
公开(公告)号：WO2005078631A1
公开(公告)日：2005-08-25
申请号：PCT/US2005/004188
申请日：2005-02-09
申请人： SIEMENS MEDICAL SOLUTIONS USA, INC. , KRISHNAN, Sriram , BI, Jinbo , RAO, R. Bharat
发明人： KRISHNAN, Sriram , BI, Jinbo , RAO, R. Bharat
IPC分类号： G06F19/00
CPC分类号： G16H50/20 , G06F19/00 , G16H50/50
摘要： Hierarchal modeling is used to distinguish one state (26, 28, 32, 34, 38, 40, 44, 46) or class from three or more classes. In a first stage, a normal (26) or other class is distinguished from a diseased (28) or other groups of classes. If the results of the first stage classification indicate diseased (28) or data within the groups of different classes, a subsequent stage of classification is performed. In a subsequent stage of classification, the data is classified to distinguish one or more other classes (32, 34, 38, 40, 44, 46) from the remaining classes. Using two or more stages, medical information is classified by eliminating one or more possible classes in each stage to finally identify a particular class (26, 28, 32, 34, 38, 40, 44, 46) most appropriate or probable for the data.
摘要翻译：分层建模用于将一个状态（26,28,32,34,38,40,44,46）或类与三个或更多个类别区分开。在第一阶段，正常（26）或其他类别与患病（28）或其他类别的组不同。如果第一阶段分类的结果表示患病（28）或不同类别的组内的数据，则进行后续分类阶段。在分类的后续阶段，数据被分类以区分一个或多个其他类别（32,34,38,40,44,46）与其余类别。使用两个或更多个阶段，通过消除每个阶段中的一个或多个可能的类别来分类医学信息，以最终确定最合适或可能的数据的特定类别（26,28,34,34,38,40,44,46）。

9. 发明申请

WO2006096364A2 PERFORMANCE ADJUSTMENTS IN MEDICAL DECISION SUPPORT SYSTEMS 审中-公开
标题翻译：医疗决策支持系统中的性能调整
公开(公告)号：WO2006096364A2
公开(公告)日：2006-09-14
申请号：PCT/US2006/006842
申请日：2006-02-27
申请人： SIEMENS MEDICAL SOLUTIONS USA, INC. , KRISHNAN, Sriram , DUNDAR, Murat , RAO, R. Bharat
发明人： KRISHNAN, Sriram , DUNDAR, Murat , RAO, R. Bharat
IPC分类号： G06F19/00
CPC分类号： G16H40/63 , G06F19/00
摘要： A user adjusts the performance for assisted diagnosis. The sensitivity, specificity or both are adjusted as desired by the physician or end-user. By adjusting the trade-offs, a decision support system may be optimized by each user or for each case, possibly avoiding a one-fits-all approach or requiring purchase of different CAD products.
摘要翻译：
用户调整辅助诊断的性能。灵敏度，特异度或两者均根据医生或最终用户的需要进行调整。通过调整权衡，决策支持系统可以由每个用户或针对每种情况进行优化，可能避免采取一劳永逸的方法或要求购买不同的CAD产品。

10. 发明申请

WO2006088983A2 MISSING DATA APPROACHES IN MEDICAL DECISION SUPPORT SYSTEMS 审中-公开
标题翻译：医疗决策支持系统丢失数据处理方法
公开(公告)号：WO2006088983A2
公开(公告)日：2006-08-24
申请号：PCT/US2006/005388
申请日：2006-02-16
申请人： SIEMENS MEDICAL SOLUTIONS USA, INC. , KRISHNAN, Sriram , RAO, R. Bharat
发明人： KRISHNAN, Sriram , RAO, R. Bharat
IPC分类号： G06F19/00
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。

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