专利快速检索-快速检索全球专利，免费商用专利数据库-IPRDB

21. 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。

22. 发明申请

US20100057651A1 Knowledge-Based Interpretable Predictive Model for Survival Analysis 有权
标题翻译：基于知识的解释性生存分析预测模型
公开(公告)号：US20100057651A1
公开(公告)日：2010-03-04
申请号：US12506583
申请日：2009-07-21
申请人： Glenn Fung , Cary Dehing-Oberije , Andreas Lubbertus Aloysius Johannes Dekker , Philippe Lambin , Shipeng Yu , Kartik Jayasurya Komati
发明人： Glenn Fung , Cary Dehing-Oberije , Andreas Lubbertus Aloysius Johannes Dekker , Philippe Lambin , Shipeng Yu , Kartik Jayasurya Komati
IPC分类号： G06F15/18 , G06N5/02
CPC分类号： G06N7/005 , A61N2005/1041
摘要： Knowledge-based interpretable predictive modeling is provided. Expert knowledge is used to seed training of a model by a machine. The expert knowledge may be incorporated as diagram information, which relates known causal relationships between predictive variables. A predictive model is trained. In one embodiment, the model operates even with a missing value for one or more variables by using the relationship between variables. For application, the model outputs a prediction, such as the likelihood of survival for two years of a lung cancer patient. A graphical representation of the model is also output. The graphical representation shows the variables and relationships between variables used to determine the prediction. The graphical representation is interpretable by a physician or other to assist in understanding.
摘要翻译：提供基于知识的可解释预测模型。专家知识用于通过机器对模型进行种子培训。专家知识可以作为图表信息并入，其涉及预测变量之间的已知因果关系。一个预测模型被训练。在一个实施例中，通过使用变量之间的关系，该模型甚至通过一个或多个变量的缺失值来运行。为了应用，该模型输出预测，例如两年的肺癌患者的生存可能性。还会输出模型的图形表示。图形表示显示用于确定预测的变量和变量之间的关系。图形表示可由医生或其他人解释，以协助理解。

23. 发明申请

US20090187522A1 System and Method for Privacy Preserving Predictive Models for Lung Cancer Survival Analysis 有权
标题翻译：用于隐私保护肺癌生存分析预测模型的系统和方法
公开(公告)号：US20090187522A1
公开(公告)日：2009-07-23
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： G06N5/02 , G06F7/06 , G06F17/30
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。

24. 发明授权

US08812240B2 Dose distribution modeling by region from functional imaging 有权
标题翻译：功能成像区域的剂量分布建模
公开(公告)号：US08812240B2
公开(公告)日：2014-08-19
申请号：US12398487
申请日：2009-03-05
申请人： Shipeng Yu , Glenn Fung , Steven Florian Petit , Hugo J. W. L. Aerts , Claudia Offermann , Michel Oellers , Philippe Lambin , Dirk de Ruysscher , Andreas Lubbertus Aloysius Johannes Dekker , Sriram Krishnan
发明人： Shipeng Yu , Glenn Fung , Steven Florian Petit , Hugo J. W. L. Aerts , Claudia Offermann , Michel Oellers , Philippe Lambin , Dirk de Ruysscher , Andreas Lubbertus Aloysius Johannes Dekker , Sriram Krishnan
IPC分类号： G01N33/48 , G01N31/00 , G06G7/48 , G06G7/58 , A61N5/10 , G06F19/00 , G06N99/00 , G06T11/00
CPC分类号： A61N5/1031 , A61N2005/1041 , G06F19/00 , G06N99/005 , G06T11/00 , G16H50/50
摘要： Functional imaging information is used to determine a probability of residual disease given a treatment. The functional imaging information shows different characteristic levels for different regions of the tumor. The probability is output for planning use and/or used to automatically determine dose by region. Using the probability, the dose may be distributed by region so that some regions receive a greater dose than other regions. This distribution by region of dose more likely treats the tumor with a same dose, allows a lesser dose to sufficient treat the tumor, and/or allows a greater dose with a lesser or no increase in risk to normal tissue. The dose plan may account for personalized tumors as each patient may have distinct tumors. Probability of dose application accuracy may also be used, so that a combined treatment probability allows efficient dose planning.
摘要翻译：功能成像信息用于确定给予治疗的残留疾病的概率。功能成像信息显示肿瘤不同区域的不同特征水平。输出用于规划使用的概率和/或用于按区域自动确定剂量。使用概率，剂量可以按区域分布，使得一些区域比其他区域接受更大的剂量。通过剂量区域的这种分布更可能以相同的剂量治疗肿瘤，允许较小剂量足以治疗肿瘤，和/或允许更大剂量对正常组织的风险较小或不增加。每个患者可能具有不同的肿瘤，剂量计划可以说明个体化肿瘤。也可以使用剂量施用精度的概率，使得组合的治疗概率允许有效的剂量计划。

25. 发明授权

US08032308B2 Modeling lung cancer survival probability after or side-effects from therapy 有权
标题翻译：建立肺癌存活概率或治疗后副作用
公开(公告)号：US08032308B2
公开(公告)日：2011-10-04
申请号：US12399274
申请日：2009-03-06
申请人： Shipeng Yu , Glenn Fung , Cary Dehing-Oberije , Dirk de Ruysscher , Sriram Krishnan , R. Bharat Rao , Philippe Lambin
发明人： Shipeng Yu , Glenn Fung , Cary Dehing-Oberije , Dirk de Ruysscher , Sriram Krishnan , R. Bharat Rao , Philippe Lambin
IPC分类号： G01N33/48 , G01N33/50
CPC分类号： G06N99/005 , A61N5/1031 , A61N2005/1041 , G06F19/00 , G16H50/50
摘要： Modeling of prognosis of survivability, side-effect, or both is provided. For example, RILI is predicted using bullae information. The amount, volume or ratio of Bullae, even alone, may indicate the likelihood of complication, such as the likelihood of significant (e.g., stage 3) pneumonitis. As another example, RILI is predicted using uptake values of an imaging agent. Standardized uptake from a functional image (e.g., FDG uptake from a positron emission image), alone or in combination with other features, may indicate the likelihood of side-effect. In another example, survivability, such as two-year survivability, is predicted using blood biomarkers. The characteristics of a patient's blood may be measured and, alone or in combination with other features, may indicate the likelihood of survival. The modeling may be for survivability, side-effect, or both and may use one or more of the blood biomarker, uptake value, and bullae features.
摘要翻译：提供了对生存能力，副作用或两者的预后的建模。例如，使用大疱信息预测RILI。 Bullae的数量，体积或比例，甚至单独可能表明并发症的可能性，例如显着（例如阶段3）肺炎的可能性。作为另一个例子，使用成像剂的摄取值来预测RILI。来自功能图像的标准摄取（例如，来自正电子发射图像的FDG摄取）单独或与其它特征组合可以指示副作用的可能性。在另一个例子中，使用血液生物标志物来预测存活能力，例如两年生存能力。可以测量患者血液的特征，单独或与其它特征组合可能表明存活的可能性。建模可以是存活性，副作用或两者，并且可以使用一种或多种血液生物标志物，摄取值和大疱特征。

26. 发明申请

US20080288292A1 System and Method for Large Scale Code Classification for Medical Patient Records 审中-公开
标题翻译：医疗病历记录大规模代码分类系统与方法
公开(公告)号：US20080288292A1
公开(公告)日：2008-11-20
申请号：US12119778
申请日：2008-05-13
申请人： Jinbo Bi , Lucian Vlad Lita , Radu Stefan Niculescu , R. Bharat Rao , Shipeng Yu
发明人： Jinbo Bi , Lucian Vlad Lita , Radu Stefan Niculescu , R. Bharat Rao , Shipeng Yu
IPC分类号： G06Q50/00 , G06F17/11 , G06F15/18
CPC分类号： G06Q50/24 , G16H50/20
摘要： A method for training classifiers for ICD-9 patient codes includes providing a set of documents regarding patient hospital visits, combining the documents for each patient visit to create a hospital visit profile, defining 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 the profiles to remove redundancy at a paragraph level and perform tokenization and sentence splitting, performing feature selection, randomly dividing the documents into training, validation, and test sets, and training 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.
摘要翻译：用于训练用于ICD-9患者代码的分类器的方法包括提供关于患者医院访问的一组文件，将用于每个患者访问的文档组合以创建医院访问简档，将特征定义为具有更大或相等的出现频率的ngram 到没有出现在标准列表中的预定值，处理简档以消除段落级别的冗余并执行标记化和句子分割，执行特征选择，将文档随机分成训练，验证和测试集合，以及使用加权脊回归训练一组二进制分类器，每个二进制分类器使用训练集针对单个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。

27. 发明授权

US07428700B2 Vision-based document segmentation 失效
标题翻译：基于视觉的文档分割
公开(公告)号：US07428700B2
公开(公告)日：2008-09-23
申请号：US10628766
申请日：2003-07-28
申请人： Ji-Rong Wen , Shipeng Yu , Deng Cai , Wei-Ying Ma
发明人： Ji-Rong Wen , Shipeng Yu , Deng Cai , Wei-Ying Ma
IPC分类号： G06F17/00
CPC分类号： G06F17/30716 , G06F17/218 , G06F17/2247
摘要： Vision-based document segmentation identifies one or more portions of semantic content of a document. The one or more portions are identified by identifying a plurality of visual blocks in the document, and detecting one or more separators between the visual blocks of the plurality of visual blocks. A content structure for the document is constructed based at least in part on the plurality of visual blocks and the one or more separators, and the content structure identifies the one or more portions of semantic content of the document. The content structure obtained using the vision-based document segmentation can optionally be used during document retrieval.
摘要翻译：基于视觉的文档分割识别文档的语义内容的一个或多个部分。通过识别文档中的多个可视块并且检测多个视觉块中的可视块之间的一个或多个分隔符来识别一个或多个部分。至少部分地基于多个可视块和一个或多个分隔符来构建文档的内容结构，并且内容结构标识文档的语义内容的一个或多个部分。使用基于视觉的文档分割获得的内容结构可以可选地在文档检索期间使用。

28. 发明申请

US20060106798A1 Vision-Based Document Segmentation 失效
标题翻译：基于视觉的文档分割
公开(公告)号：US20060106798A1
公开(公告)日：2006-05-18
申请号：US11275488
申请日：2006-01-09
申请人： Ji-Rong Wen , Shipeng Yu , Deng Cai , Wei-Ying Ma
发明人： Ji-Rong Wen , Shipeng Yu , Deng Cai , Wei-Ying Ma
IPC分类号： G06F17/30 , G06F7/00
CPC分类号： G06F17/30716 , G06F17/218 , G06F17/2247
摘要： Vision-based document segmentation identifies one or more portions of semantic content of a document. The one or more portions are identified by identifying a plurality of visual blocks in the document, and detecting one or more separators between the visual blocks of the plurality of visual blocks. A content structure for the document is constructed based at least in part on the plurality of visual blocks and the one or more separators, and the content structure identifies the one or more portions of semantic content of the document. The content structure obtained using the vision-based document segmentation can optionally be used during document retrieval.
摘要翻译：基于视觉的文档分割识别文档的语义内容的一个或多个部分。通过识别文档中的多个可视块并且检测多个视觉块中的可视块之间的一个或多个分隔符来识别一个或多个部分。至少部分地基于多个可视块和一个或多个分隔符来构建文档的内容结构，并且内容结构标识文档的语义内容的一个或多个部分。使用基于视觉的文档分割获得的内容结构可以可选地在文档检索期间使用。

29. 发明申请

US20050028077A1 Vision-based document segmentation 失效
标题翻译：基于视觉的文档分割
公开(公告)号：US20050028077A1
公开(公告)日：2005-02-03
申请号：US10628766
申请日：2003-07-28
申请人： Ji-Rong Wen , Shipeng Yu , Deng Cai , Wei-Ying Ma
发明人： Ji-Rong Wen , Shipeng Yu , Deng Cai , Wei-Ying Ma
IPC分类号： G06F15/00 , G06F20060101 , G06F17/00 , G06F17/21 , G06F17/22 , G06F17/30 , G06K9/72 , G06K15/00
CPC分类号： G06F17/30716 , G06F17/218 , G06F17/2247
摘要： Vision-based document segmentation identifies one or more portions of semantic content of a document. The one or more portions are identified by identifying a plurality of visual blocks in the document, and detecting one or more separators between the visual blocks of the plurality of visual blocks. A content structure for the document is constructed based at least in part on the plurality of visual blocks and the one or more separators, and the content structure identifies the one or more portions of semantic content of the document. The content structure obtained using the vision-based document segmentation can optionally be used during document retrieval.
摘要翻译：基于视觉的文档分割识别文档的语义内容的一个或多个部分。通过识别文档中的多个可视块并且检测多个视觉块中的可视块之间的一个或多个分隔符来识别一个或多个部分。至少部分地基于多个可视块和一个或多个分隔符来构建文档的内容结构，并且内容结构标识文档的语义内容的一个或多个部分。使用基于视觉的文档分割获得的内容结构可以可选地在文档检索期间使用。

30. 发明授权

US07613995B2 Vision-based document segmentation 失效
标题翻译：基于视觉的文档分割
公开(公告)号：US07613995B2
公开(公告)日：2009-11-03
申请号：US11275488
申请日：2006-01-09
申请人： Ji-Rong Wen , Shipeng Yu , Deng Cai , Wei-Ying Ma
发明人： Ji-Rong Wen , Shipeng Yu , Deng Cai , Wei-Ying Ma
IPC分类号： G06F17/00
CPC分类号： G06F17/30716 , G06F17/218 , G06F17/2247
摘要： Vision-based document segmentation identifies one or more portions of semantic content of a document. The one or more portions are identified by identifying a plurality of visual blocks in the document, and detecting one or more separators between the visual blocks of the plurality of visual blocks. A content structure for the document is constructed based at least in part on the plurality of visual blocks and the one or more separators, and the content structure identifies the one or more portions of semantic content of the document. The content structure obtained using the vision-based document segmentation can optionally be used during document retrieval.
摘要翻译：基于视觉的文档分割识别文档的语义内容的一个或多个部分。通过识别文档中的多个可视块并且检测多个视觉块中的可视块之间的一个或多个分隔符来识别一个或多个部分。至少部分地基于多个可视块和一个或多个分隔符来构建文档的内容结构，并且内容结构标识文档的语义内容的一个或多个部分。使用基于视觉的文档分割获得的内容结构可以可选地在文档检索期间使用。

你已经成功收藏专利！

检索式保存成功!

IPRDB

热门服务

关于我们

友情链接

联系方式