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21. 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。

22. 发明授权

US07756313B2 System and method for computer aided detection via asymmetric cascade of sparse linear classifiers 有权
标题翻译：通过稀疏线性分类器的不对称级联进行计算机辅助检测的系统和方法
公开(公告)号：US07756313B2
公开(公告)日：2010-07-13
申请号：US11592869
申请日：2006-11-03
申请人： Jinbo Bi , Senthil Periaswamy , Kazunori Okada , Toshiro Kubota , Glenn Fung , Marcos Salganicoff , R. Bharat Rao
发明人： Jinbo Bi , Senthil Periaswamy , Kazunori Okada , Toshiro Kubota , Glenn Fung , Marcos Salganicoff , R. Bharat Rao
IPC分类号： G06K9/00
CPC分类号： G06K9/6256 , G06K9/6282
摘要： A method for computer aided detection of anatomical abnormalities in medical images includes providing a plurality of abnormality candidates and features of said abnormality candidates, and classifying said abnormality candidates as true positives or false positives using a hierarchical cascade of linear classifiers of the form sign(wTx+b), wherein x is a feature vector, w is a weighting vector and b is a model parameter, wherein different weights are used to penalize false negatives and false positives, and wherein more complex features are used for each successive stage of said cascade of classifiers.
摘要翻译：一种用于计算机辅助检测医学图像中的解剖异常的方法，包括提供所述异常候选的多个异常候选和特征，并且使用形式符号的线性分类器（wTx）的分级级联将所述异常候选分类为真正的肯定或假阳性 + b），其中x是特征向量，w是加权向量，b是模型参数，其中使用不同权重来惩罚假否定和假阳性，并且其中对于级联的每个连续阶段使用更复杂的特征的分类器。

23. 发明申请

US20050058338A1 Incorporating spatial knowledge for classification 有权
标题翻译：结合空间知识进行分类
公开(公告)号：US20050058338A1
公开(公告)日：2005-03-17
申请号：US10915076
申请日：2004-08-10
申请人： Arun Krishnan , Glenn Fung , Jonathan Stoeckel
发明人： Arun Krishnan , Glenn Fung , Jonathan Stoeckel
IPC分类号： G06F19/00 , G06K9/00 , G06K9/62 , G06K9/68 , G06T7/00
CPC分类号： G06T7/0012 , G06K9/6807 , G06T2207/10072 , G06T2207/20012 , G06T2207/30064
摘要： We propose using different classifiers based on the spatial location of the object. The intuitive idea behind this approach is that several classifiers may learn local concepts better than a “universal” classifier that covers the whole feature space. The use of local classifiers ensures that the objects of a particular class have a higher degree of resemblance within that particular class. The use of local classifiers also results in memory, storage and performance improvements, especially when the classifier is kernel-based. As used herein, the term “kernel-based classifier” refers to a classifier where a mapping function (i.e., the kernel) has been used to map the original training data to a higher dimensional space where the classification task may be easier.
摘要翻译：我们建议基于对象的空间位置使用不同的分类器。这种方法背后的直观思想是，几个分类器可以比涵盖整个特征空间的“通用”分类器更好地学习局部概念。使用本地分类器确保特定类的对象在该特定类中具有更高程度的相似度。使用本地分类器也会导致内存，存储和性能改进，特别是当分类器是基于内核的时候。如本文所使用的，术语“基于内核的分类器”是指其中已经使用映射函数（即，内核）将原始训练数据映射到更高维度空间的分类器，其中分类任务可以更容易。

24. 发明申请

US20140095204A1 AUTOMATED MEDICAL COHORT DETERMINATION 审中-公开
标题翻译：自动化医疗协调决定
公开(公告)号：US20140095204A1
公开(公告)日：2014-04-03
申请号：US14037469
申请日：2013-09-26
申请人： Glenn Fung , Balaji Krishnapuram , Faisal Farooq , Shipeng Yu , Bharat R. Rao , Vikram Anand
发明人： Glenn Fung , Balaji Krishnapuram , Faisal Farooq , Shipeng Yu , Bharat R. Rao , Vikram Anand
IPC分类号： G06F19/00
CPC分类号： G16H50/20 , G16H50/70
摘要： Inclusion of a patient in a medical category is determined by triggering an analysis of an electronic medical record of the patient in response to an input of data into the electronic medical record. Identifying characteristics that indicate inclusion in the medical category with the analysis, and determining a probability the patient belongs to the medical category based on the identified characteristics.
摘要翻译：通过响应于将数据输入到电子医疗记录中的触发对患者的电子医疗记录的分析来确定医疗类别中的患者的包含。通过分析来识别指示包含在医疗类别中的特征，并且基于所识别的特征来确定患者属于医疗类别的概率。

25. 发明申请

US20140095203A1 MEDICAL WORKFLOW DETERMINATION AND OPTIMIZATION 审中-公开
标题翻译：医疗工作流程确定与优化
公开(公告)号：US20140095203A1
公开(公告)日：2014-04-03
申请号：US14035989
申请日：2013-09-25
申请人： Vikram Anand , Balaji Krishnapuram , Glenn Fung , Shipeng Yu , Faisal Farooq , Jonathan D. Emanuele
发明人： Vikram Anand , Balaji Krishnapuram , Glenn Fung , Shipeng Yu , Faisal Farooq , Jonathan D. Emanuele
IPC分类号： G06Q50/24 , G06Q10/06
CPC分类号： G16H40/20 , G06F19/325 , G06Q10/0633 , G06Q50/22 , G06Q50/24 , G16H10/60 , G16H50/50
摘要： Workflows for medical entities are determined and evaluated by determining a plurality of medical tasks based on an analysis of a plurality of electronic medical records of a medical entity. A workflow of the medical entity is determined based on a sequence of medical tasks, the sequence determined based on the analysis of the plurality of electronic medical records, and an evaluation of the workflow is performed based on a predefined criterion.
摘要翻译：通过基于对医疗实体的多个电子病历的分析来确定多个医疗任务来确定和评估医疗实体的工作流程。基于医疗任务的顺序确定医疗实体的工作流程，该顺序是基于多个电子医疗记录的分析确定的顺序，并且基于预定义的标准来执行工作流程的评估。

26. 发明申请

US20140088989A1 Rapid Learning Community for Predictive Models of Medical Knowledge 审中-公开
标题翻译：医学知识预测模型快速学习社区
公开(公告)号：US20140088989A1
公开(公告)日：2014-03-27
申请号：US14027494
申请日：2013-09-16
申请人： Balaji Krishnapuram , Bharat R. Rao , Glenn Fung , Vikram Anand , Faisal Farooq , Wolfgang Wiessler , Shipeng Yu
发明人： Balaji Krishnapuram , Bharat R. Rao , Glenn Fung , Vikram Anand , Faisal Farooq , Wolfgang Wiessler , Shipeng Yu
IPC分类号： G06F19/00
CPC分类号： G16H50/70 , G16H50/50
摘要： A predictive model of medical knowledge is trained from patient data of multiple different medical centers. The predictive model is machine learnt from routine patient data from multiple medical centers. Distributed learning avoids transfer of the patient data from any of the medical centers. Each medical center trains the predictive model from the local patient data. The learned statistics, and not patient data, are transmitted to a central server. The central server reconciles the statistics and proposes new statistics to each of the local medical centers. In an iterative approach, the predictive model is developed without transfer of patient data but with statistics responsive to patient data available from multiple medical centers. To assure comfort with the process, the transmitted statistics may be in a human readable format.
摘要翻译：医学知识的预测模型是从多个不同医疗中心的患者数据进行培训。预测模型是从多个医疗中心的常规患者数据获得的机器。分布式学习避免了从任何医疗中心转移患者数据。每个医疗中心从当地患者数据中训练预测模型。学习的统计信息而不是患者数据被传送到中央服务器。中央服务器统计统计数据，并向每个当地医疗中心提出新的统计数据。在迭代方法中，预测模型是在没有转移患者数据的情况下开发的，但是对于可从多个医疗中心获得的患者数据进行统计。为了确保该过程的舒适度，所发送的统计数据可以是人类可读的格式。

27. 发明授权

US08027939B2 Automatic labeler assignment using a model built from multi-labeler data 有权
标题翻译：使用由多标签器数据构建的模型进行自动标签分配
公开(公告)号：US08027939B2
公开(公告)日：2011-09-27
申请号：US12893008
申请日：2010-09-29
申请人： Glenn Fung , Romer E. Rosales , Jennifer G. Dy , Yan Yan
发明人： Glenn Fung , Romer E. Rosales , Jennifer G. Dy , Yan Yan
IPC分类号： G06F15/18 , G06K9/00 , G06K9/62
CPC分类号： G06K9/6256 , G06F19/00 , G06K9/6226 , G06K9/6262 , G06N99/005 , G16H40/20
摘要： A method, including receiving multi-labeler data that includes data points labeled by a plurality of labelers; building a model from the multi-labeler data, wherein the model includes an input variable that corresponds to the data points, a label variable that corresponds to true labels for the data points, and variables for the labels given by the labelers; and executing the model, in response to receiving new data points, to determine a level of expertise of the labelers for the new data points.
摘要翻译：一种方法，包括接收包括由多个标签器标记的数据点的多标签器数据; 从所述多标签器数据构建模型，其中所述模型包括对应于所述数据点的输入变量，对应于所述数据点的真实标签的标签变量以及由所述标签器给出的标签的变量; 并且响应于接收到新的数据点执行该模型，以确定新数据点的标签器的专业水平。

28. 发明授权

US07960114B2 Gene signature of early hypoxia to predict patient survival 有权
标题翻译：早期缺氧的基因特征预测患者生存
公开(公告)号：US07960114B2
公开(公告)日：2011-06-14
申请号：US12113373
申请日：2008-05-01
申请人： Renaud G. Seigneuric , Maud H. W. Starmans , Glenn Fung , Balaji Krishnapuram , Dimitry S. A. Nuyten , Arie van Erk , Michael Gaston Magagnin , Kasper M. Rouschop , Sriram Krishnan , R. Bharat Rao , Christoffel Theodorus Anthonius Evelo , Adrian Campbell Begg , Bradly G. Wouters , Philippe Lambin
发明人： Renaud G. Seigneuric , Maud H. W. Starmans , Glenn Fung , Balaji Krishnapuram , Dimitry S. A. Nuyten , Arie van Erk , Michael Gaston Magagnin , Kasper M. Rouschop , Sriram Krishnan , R. Bharat Rao , Christoffel Theodorus Anthonius Evelo , Adrian Campbell Begg , Bradly G. Wouters , Philippe Lambin
IPC分类号： C12Q1/68
CPC分类号： C12Q1/6886 , C12Q2600/106 , C12Q2600/118
摘要： The present invention provides methods and compositions for predicting patient responses to cancer treatment using hypoxia gene signatures. 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：本发明提供了使用缺氧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。

29. 发明申请

US20110071967A1 Automatic Labeler Assignment 有权
标题翻译：自动贴标机分配
公开(公告)号：US20110071967A1
公开(公告)日：2011-03-24
申请号：US12893008
申请日：2010-09-29
申请人： Glenn Fung , Romer E. Rosales , Jennifer G. Dy , Yan Yan
发明人： Glenn Fung , Romer E. Rosales , Jennifer G. Dy , Yan Yan
IPC分类号： G06F15/18
CPC分类号： G06K9/6256 , G06F19/00 , G06K9/6226 , G06K9/6262 , G06N99/005 , G16H40/20
摘要： A method, including receiving multi-labeler data that includes data points labeled by a plurality of labelers; building a model from the multi-labeler data, wherein the model includes an input variable that corresponds to the data points, a label variable that corresponds to true labels for the data points, and variables for the labels given by the labelers; and executing the model, in response to receiving new data points, to determine a level of expertise of the labelers for the new data points.
摘要翻译：一种方法，包括接收包括由多个标签器标记的数据点的多标签器数据; 从所述多标签器数据构建模型，其中所述模型包括对应于所述数据点的输入变量，对应于所述数据点的真实标签的标签变量以及由所述标签器给出的标签的变量; 并且响应于接收到新的数据点执行该模型，以确定新数据点的标签器的专业水平。

30. 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。

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