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1. 发明申请

WO2009098627A1 APPARATUS FOR MEASURING AND PREDICTING PATIENTS' RESPIRATORY STABILITY 审中-公开
标题翻译：用于测量和预测患者呼吸稳定性的装置
公开(公告)号：WO2009098627A1
公开(公告)日：2009-08-13
申请号：PCT/IB2009/050402
申请日：2009-02-02
申请人： KONINKLIJKE PHILIPS ELECTRONICS N.V. , SAEED, Mohammed , LEE, Kwok, Pun , ENNETT, Colleen, M. , ESHELMAN, Larry , NIELSEN, Larry , GROSS, Brian
发明人： SAEED, Mohammed , LEE, Kwok, Pun , ENNETT, Colleen, M. , ESHELMAN, Larry , NIELSEN, Larry , GROSS, Brian
IPC分类号： A61B5/08 , A61B5/0205 , G08B21/04 , G06F19/00
CPC分类号： A61B5/14551 , A61B5/0205 , A61B5/0816 , A61B5/085 , A61B5/087 , A61B5/7275 , A61M16/0051 , A61M16/021 , A61M16/12 , A61M2016/0027 , A61M2205/18 , A61M2205/3561 , A61M2205/3584 , A61M2205/3592 , A61M2205/502 , A61M2205/52 , A61M2230/06 , A61M2230/205 , A61M2230/42 , G06F19/00 , G06Q50/22 , G16H50/20
摘要： An apparatus (10) for predicting patient respiratory stability includes a patient data memory (24) which stores patient data for a patient (12) and an analyzer (34) in communication with the memory computes a measure of patient respiratory stability. The analyzer applies one or more rules to the patient data that are based on a plurality of parameters which in combination, have been identified as being predictive of patient respiratory instability, such as mean airway pressure (MAWP), plateau pressure (PP), arterial oxygen saturation (SaO2 or SpO2), and heart rate (HR). Based on the application of the rules, the analyzer determines the measure of patient respiratory stability.
摘要翻译：用于预测患者呼吸稳定性的装置（10）包括存储患者（12）的患者数据的患者数据存储器（24）和与存储器通信的分析器（34）计算患者呼吸稳定性的量度。分析仪对患者数据应用一个或多个规则，所述规则基于多个参数，所述多个参数组合已被识别为预测患者呼吸不稳定性，例如平均气道压力（MAWP），平台压力（PP），动脉氧饱和度（SaO2或SpO2）和心率（HR）。根据规则的应用，分析仪确定患者呼吸稳定性的量度。

2. 发明申请

WO2008017991A3 METHODS AND APPARATUS TO INTEGRATE SYSTEMATIC DATA SCALING INTO GENETIC ALGORITHM-BASED FEATURE SUBSET SELECTION 审中-公开
标题翻译：用于将系统数据缩放整合为基于遗传算法的特征子选择的方法和设备
公开(公告)号：WO2008017991A3
公开(公告)日：2008-10-30
申请号：PCT/IB2007053048
申请日：2007-08-02
申请人： KONINKL PHILIPS ELECTRONICS NV , ZHAO LUYIN , BOROCZKY LILLA , LEE KWOK PUN
发明人： ZHAO LUYIN , BOROCZKY LILLA , LEE KWOK PUN
IPC分类号： G06N3/12 , G06F19/00
CPC分类号： G06N3/126 , G06K9/6229 , G16H50/20
摘要： Methods and apparatus for training a system for developing a process of data mining, false positive reduction, computer-aided detection, computer-aided diagnosis and artificial intelligence are provided. A method includes choosing a training set from a set of training cases using systematic data scaling and creating a classifier based on the training set using a classification method. The classifier yields fewer false positives. The method is suitable for use with a variety of data mining techniques including support vector machines, neural networks and decision trees.
摘要翻译：提供了用于训练用于开发数据挖掘，假阳性减少，计算机辅助检测，计算机辅助诊断和人工智能的系统的方法和装置。一种方法包括使用系统化数据缩放从一组训练案例中选择一个训练集并且使用分类方法基于训练集创建分类器。分类器产生的误报较少。该方法适用于各种数据挖掘技术，包括支持向量机，神经网络和决策树。

3. 发明申请

WO2006054269A2 SYSTEM AND METHOD FOR FALSE POSITIVE REDUCTION IN COMPUTER-AIDED DETECTION (CAD) USING A SUPPORT VECTOR MACHINE (SVM) 审中-公开
标题翻译：使用支持向量机（SVM）的计算机辅助检测（CAD）中的虚拟正减少的系统和方法
公开(公告)号：WO2006054269A2
公开(公告)日：2006-05-26
申请号：PCT/IB2005053824
申请日：2005-11-18
申请人： KONINKL PHILIPS ELECTRONICS NV , BOROCZKY LILLA , ZHAO LUYIN , LEE KWOK PUN
发明人： BOROCZKY LILLA , ZHAO LUYIN , LEE KWOK PUN
CPC分类号： G06K9/6228
摘要： A method for computer aided detection (CAD) and classification of regions of interest detected within HRCT medical image data includes post-processing machine learning to maximize specificity and sensitivity of the classification to realize a reduction in number of false positive detections reported. The method includes training a classifier on a set of medical image training data selected to include a number of true and false regions, wherein the true and false regions are identified by a CAD process, and automatically segmented, wherein the segmented training regions are reviewed by at least one specialist to classify each training region for its ground truth, i.e., true or false, essentially qualifying the automatic segmentation, wherein a feature pool is identified and extracted from each segmented region, and wherein the pool of features is processed by genetic algorithm to identify an optimal feature subset, which subset is used to train a support vector machine, detecting, within non- training medical image data, regions that are candidates for classification, segmenting the candidate regions, extracting a set of features from each segmented candidate regions and classifying the candidate region using the support vector machine after training in accordance with the optimal feature subset, and processing the set of candidate features.
摘要翻译：在HRCT医学图像数据中检测到的计算机辅助检测（CAD）和感兴趣区域分类的方法包括后处理机器学习，以最大限度地提高分类的特异性和灵敏度，实现报告的假阳性检测数量的减少。该方法包括对选择包括多个真假区域的一组医学图像训练数据对分类器进行训练，其中真实区域和假区域由CAD过程识别并自动分段，其中分割的训练区域由至少一名专家将每个训练区域分类为其真实的，即真实的或虚假的，基本上限定了自动分割，其中从每个分段区域识别和提取特征池，并且其中特征池通过遗传算法识别最佳特征子集，该子集用于训练支持向量机，在非训练医学图像数据内检测作为分类候选的区域，分割候选区域，从每个分割的候选区域提取一组特征并根据最优f对训练后使用支持向量机对候选区域进行分类特征子集，并处理候选特征集合。

4. 发明申请

WO2006054271A3 FALSE POSITIVE REDUCTION IN COMPUTER -ASSISTED DETECTION ( CAD) 审中-公开
标题翻译：计算机检测中的积极减少（CAD）
公开(公告)号：WO2006054271A3
公开(公告)日：2007-04-05
申请号：PCT/IB2005053835
申请日：2005-11-21
申请人： KONINKL PHILIPS ELECTRONICS NV , BOROCZKY LILLA , LEE KWOK PUN , ZHAO LUYIN
发明人： BOROCZKY LILLA , LEE KWOK PUN , ZHAO LUYIN
IPC分类号： G06T7/00 , G06K9/62
CPC分类号： G06T7/0012 , G06K9/3233 , G06T2207/10081 , G06T2207/20081 , G06T2207/30061
摘要： A method for computer aided detection (CAD) and classification of regions of interest detected within HRCT medical image data includes post-CAD machine learning techniques applied to maximize specificity and sensitivity of identification of a region/volume as being a nodule or non-nodule. The regions are identified by a CAD process, and automatically segmented. A feature pool is identified and extracted from each segmented region, and processed by genetic algorithm to identify an optimal feature subset, which subset is used to train the support vector machine to classify candidate region/volumes found within non-training data.
摘要翻译：在HRCT医学图像数据中检测到的计算机辅助检测（CAD）和感兴趣区域的分类方法包括后CAD机器学习技术，其应用于将区域/体积的识别的特异性和敏感性最大化为结节或非结节。区域由CAD过程识别，并自动分段。从每个分段区域识别和提取特征池，并通过遗传算法进行处理以识别最优特征子集，该子集用于训练支持向量机以对在非训练数据内发现的候选区域/体积进行分类。

5. 发明申请

WO2012140547A1 STEPPED ALARM METHOD FOR PATIENT MONITORS 审中-公开
标题翻译：患者监护仪的步进报警方法
公开(公告)号：WO2012140547A1
公开(公告)日：2012-10-18
申请号：PCT/IB2012/051654
申请日：2012-04-04
申请人： KONINKLIJKE PHILIPS ELECTRONICS N.V. , ESHELMAN, Larry, James , FEDDES, Bastiaan , FLOWER, Abigail, Acton , LAMBERT, Nicolaas , LEE, Kwok, Pun , TJAN, Davy, Hin, Tjiang , DE WAELE, Stijn , GROSS, Brian, David , FRASSICA, Joseph, J. , NIELSEN, Larry , SAEED, Mohammed , CAO, Hanqing
发明人： ESHELMAN, Larry, James , FEDDES, Bastiaan , FLOWER, Abigail, Acton , LAMBERT, Nicolaas , LEE, Kwok, Pun , TJAN, Davy, Hin, Tjiang , DE WAELE, Stijn , GROSS, Brian, David , FRASSICA, Joseph, J. , NIELSEN, Larry , SAEED, Mohammed , CAO, Hanqing
IPC分类号： A61B5/00 , G08B21/02
CPC分类号： G08B21/02 , A61B5/14551 , A61B5/746 , G06F19/00 , G06F19/3418 , G08B21/04 , G16H50/20
摘要： A system (202) generates patient alarms using a stepped alarm scheme. The system (202) includes one or more processors (220) programmed to receive physiological scores and/or physiological parameter values; compare the physiological scores and/or the physiological parameter values to a plurality of alarm levels; in response to a physiological score and/or physiological parameter value falling within an uninhibited zone of the alarm levels, issue an alarm; and set a first inhibition period for the uninhibited alarm level after issuing the alarm.
摘要翻译：系统（202）使用阶梯式报警方案产生患者报警。系统（202）包括被编程为接收生理分数和/或生理参数值的一个或多个处理器（220）将生理学分数和/或生理参数值与多个报警水平进行比较; 响应于落在警报水平的不受限区域内的生理评分和/或生理参数值，发出报警; 并在发出报警后设置不受限制的报警级别的第一个禁止期。

6. 发明申请

WO2008017991A2 METHODS AND APPARATUS TO INTEGRATE SYSTEMATIC DATA SCALING INTO GENETIC ALGORITHM-BASED FEATURE SUBSET SELECTION 审中-公开
标题翻译：将系统数据整合到基于遗传算法的特征选择中的方法和装置
公开(公告)号：WO2008017991A2
公开(公告)日：2008-02-14
申请号：PCT/IB2007/053048
申请日：2007-08-02
申请人： KONINKLIJKE PHILIPS ELECTRONICS, N.V. , ZHAO, Luyin , BOROCZKY, Lilla , LEE, Kwok Pun
发明人： ZHAO, Luyin , BOROCZKY, Lilla , LEE, Kwok Pun
IPC分类号： G06N3/12 , G06F19/00
CPC分类号： G06N3/126 , G06F19/00 , G06K9/6229 , G16H50/20
摘要： Methods and apparatus for training a system for developing a process of data mining, false positive reduction, computer-aided detection, computer-aided diagnosis and artificial intelligence are provided. A method includes choosing a training set from a set of training cases using systematic data scaling and creating a classifier based on the training set using a classification method. The classifier yields fewer false positives. The method is suitable for use with a variety of data mining techniques including support vector machines, neural networks and decision trees.
摘要翻译：提供了开发数据挖掘，假阳性减少，计算机辅助检测，计算机辅助诊断和人工智能过程的系统培训方法和装置。一种方法包括使用系统数据缩放从一组训练样本中选择训练集，并使用分类方法基于训练集创建分类器。分类器产生较少的假阳性。该方法适用于各种数据挖掘技术，包括支持向量机，神经网络和决策树。

7. 发明申请

WO2006054272A3 A STRATIFICATION METHOD FOR OVERCOMING UNBALANCED CASE NUMBERS IN COMPUTER-AIDED LUNG NODULE FALSE POSITIVE REDUCTION 审中-公开
公开(公告)号：WO2006054272A3
公开(公告)日：2006-05-26
申请号：PCT/IB2005/053843
申请日：2005-11-21
申请人： KONINKLIJKE PHILIPS ELECTRONICS, N.V. , ZHAO, Luyin , LEE, Kwok Pun , BOROCZKY, Lilla
发明人： ZHAO, Luyin , LEE, Kwok Pun , BOROCZKY, Lilla
IPC分类号： G06K9/62 , G06T7/00
摘要： A method for computer aided detection (CAD) and classification of regions of interest detected within HRCT medical image data. The method includes post-CAD machine learning techniques applied to maximize specificity and sensitivity of identification of a region/volume as being a nodule or non-nodule. The regions are identified by a CAD process, and automatically segmented. A feature pool is identified and extracted from each segmented region, and processed by genetic algorithm to identify an optimal feature subset, wherein a data stratification method is used to balance the number of cases in different classes. The subset determined by GA is used to train the support vector machine to classify candidate region/volumes found within non-training data.

8. 发明申请

WO2006054272A2 A STRATIFICATION METHOD FOR OVERCOMING UNBALANCED CASE NUMBERS IN COMPUTER-AIDED LUNG NODULE FALSE POSITIVE REDUCTION 审中-公开
标题翻译：计算机辅助肺泡假性病例中不合格病例数的分层法
公开(公告)号：WO2006054272A2
公开(公告)日：2006-05-26
申请号：PCT/IB2005053843
申请日：2005-11-21
申请人： KONINKL PHILIPS ELECTRONICS NV , ZHAO LUYIN , LEE KWOK PUN , BOROCZKY LILLA
发明人： ZHAO LUYIN , LEE KWOK PUN , BOROCZKY LILLA
IPC分类号： G06K9/62 , G06T7/00
CPC分类号： G06K9/6269 , G06K9/6228 , G06T7/0012 , G06T2207/30061
摘要： A method for computer aided detection (CAD) and classification of regions of interest detected within HRCT medical image data. The method includes post-CAD machine learning techniques applied to maximize specificity and sensitivity of identification of a region/volume as being a nodule or non-nodule. The regions are identified by a CAD process, and automatically segmented. A feature pool is identified and extracted from each segmented region, and processed by genetic algorithm to identify an optimal feature subset, wherein a data stratification method is used to balance the number of cases in different classes. The subset determined by GA is used to train the support vector machine to classify candidate region/volumes found within non-training data.
摘要翻译：一种用于在HRCT医学图像数据内检测到的感兴趣区域的计算机辅助检测（CAD）和分类的方法。该方法包括CAD后机器学习技术，该技术用于最大化将区域/体积识别为结节或非结节的特异性和灵敏度。这些区域由CAD过程识别，并自动分段。从每个分割区域中识别并提取特征池，并通过遗传算法处理以识别最优特征子集，其中使用数据分层方法来平衡不同类别中的病例数量。由GA确定的子集用于训练支持向量机以对非训练数据内发现的候选区域/卷进行分类。

9. 发明申请

WO2006054267A1 SYSTEM AND METHOD FOR AUTOMATED DETECTION AND SEGMENTATION OF TUMOR BOUNDARIES WITHIN MEDICAL IMAGING DATA 审中-公开
标题翻译：医学成像数据中肿瘤边界自动检测和分段的系统与方法
公开(公告)号：WO2006054267A1
公开(公告)日：2006-05-26
申请号：PCT/IB2005/053822
申请日：2005-11-18
申请人： KONINKLIJKE PHILIPS ELECTRONICS, N.V. , ZHAO, Luyin , LEE, Kwok Pun
发明人： ZHAO, Luyin , LEE, Kwok Pun
IPC分类号： G06T7/00 , G06K9/62
CPC分类号： G06T7/0012 , G06K9/6255 , G06T7/12 , G06T2207/10081 , G06T2207/10132 , G06T2207/20081 , G06T2207/30068 , G06T2207/30096
摘要： A method for segmenting regions within a medical image includes evaluating a set of candidate segmentations generated from an initial segmentation. Based on distance calculations for each candidate using derivative segmentations, the best candidate is recommended to clinician if it is better than the initial segmentation. This recommender realizes a most stable segmentation that will benefit follow-up computer aided diagnosis (i.e. classifying lesion to benign/malignant).
摘要翻译：用于分割医学图像内的区域的方法包括评估从初始分割生成的一组候选分割。基于使用导数分割的每个候选者的距离计算，如果优于初始分割，则建议临床医生使用最佳候选者。该推荐者实现了最有利的后续计算机辅助诊断（即将病变分类为良性/恶性）的稳定分割。

10. 发明申请

WO2006054271A2 FALSE POSITIVE REDUCTION IN COMPUTER-ASSISTED DETECTION (CAD) WITH NEW 3D FEATURES 审中-公开
标题翻译：在新的3D特征的计算机辅助检测（CAD）中虚拟正确的减少
公开(公告)号：WO2006054271A2
公开(公告)日：2006-05-26
申请号：PCT/IB2005/053835
申请日：2005-11-21
申请人： KONINKLIJKE PHILIPS ELECTRONICS, N.V. , BOROCZKY, Lilla , LEE, Kwok, Pun , ZHAO, Luyin
发明人： BOROCZKY, Lilla , LEE, Kwok, Pun , ZHAO, Luyin
CPC分类号： G06T7/0012 , G06K9/3233 , G06T2207/10081 , G06T2207/20081 , G06T2207/30061
摘要： A method for computer aided detection (CAD) and classification of regions of interest detected within HRCT medical image data includes post-CAD machine learning techniques applied to maximize specificity and sensitivity of identification of a region/volume as being a nodule or non-nodule. The regions are identified by a CAD process, and automatically segmented. A feature pool is identified and extracted from each segmented region, and processed by genetic algorithm to identify an optimal feature subset, which subset is used to train the support vector machine to classify candidate region/volumes found within non-training data.
摘要翻译：在HRCT医学图像数据中检测到的计算机辅助检测（CAD）和感兴趣区域的分类方法包括后CAD机器学习技术，其应用于将区域/体积的识别的特异性和敏感性最大化为结节或非结节。区域由CAD过程识别，并自动分段。从每个分段区域识别和提取特征池，并通过遗传算法进行处理以识别最优特征子集，该子集用于训练支持向量机以对在非训练数据内发现的候选区域/体积进行分类。

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