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1.发明申请METHODS AND SYSTEMS FOR IDENTIFYING PATIENTS WITH MILD CONGNITIVE IMPAIRMENT AT RISK OF CONVERTING TO ALZHEIMER'S 有权用于识别患有转化为阿尔茨海默氏症的风险的患者的方法和系统公开(公告)号：US20130275350A1
公开(公告)日：2013-10-17
申请号：US13995284
申请日：2011-12-09
IPC分类号： G06N99/00
CPC分类号： G06N99/005 , G06F19/00 , G16H50/20 , G16H50/70
摘要： Methods and systems for selecting a cohort group or a patient at risk from a population of patients with mild cognitive impairment. The methods include using a computer configured to perform the steps: receiving normalized learning data from a portion of the population of patients; tuning a set of decision trees on the normalized learning data; receiving patient data from one or more patients of the population, wherein the patient data is independent from the learning data; classifying the patient data with the tuned set of decision trees to obtain patient threshold values; and displaying the patient threshold values.
摘要翻译： 从轻度认知障碍患者群体选择一个队列组或有风险的患者的方法和系统。 所述方法包括使用被配置为执行以下步骤的计算机：从患者群体的一部分接收标准化学习数据; 调整一组关于规范化学习数据的决策树; 从所述群体的一个或多个患者接收患者数据，其中所述患者数据独立于所述学习数据; 用调整好的一组决策树对患者数据进行分类以获得患者阈值; 并显示患者的阈值。
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2.发明授权Methods and systems for identifying patients with mild cognitive impairment at risk of converting to alzheimer's 有权用于鉴别患有转化为阿尔茨海默病风险的轻度认知障碍患者的方法和系统公开(公告)号：US09367817B2
公开(公告)日：2016-06-14
申请号：US13995284
申请日：2011-12-09
CPC分类号： G06N99/005 , G06F19/00 , G16H50/20 , G16H50/70
摘要： Methods and systems for selecting a cohort group or a patient at risk from a population of patients with mild cognitive impairment. The methods include using a computer configured to perform the steps: receiving normalized learning data from a portion of the population of patients; tuning a set of decision trees on the normalized learning data; receiving patient data from one or more patients of the population, wherein the patient data is independent from the learning data; classifying the patient data with the tuned set of decision trees to obtain patient threshold values; and displaying the patient threshold values.
摘要翻译： 从轻度认知障碍患者群体选择一个队列组或有风险的患者的方法和系统。 所述方法包括使用被配置为执行以下步骤的计算机：从患者群体的一部分接收标准化学习数据; 对规范化学习数据调整一组决策树; 从所述群体的一个或多个患者接收患者数据，其中所述患者数据独立于所述学习数据; 用调整好的一组决策树对患者数据进行分类以获得患者阈值; 并显示患者的阈值。
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3.发明申请DETECTION OF ERRORS IN THE INFERENCE ENGINE OF A CLINICAL DECISION SUPPORT SYSTEM 有权检测临床决策支持系统的信号引擎中的错误公开(公告)号：US20100280847A1
公开(公告)日：2010-11-04
申请号：US12747595
申请日：2008-12-10
申请人： James David Schaffer
发明人： James David Schaffer
IPC分类号： G06F19/00
CPC分类号： G06F19/345 , G06F19/00 , G06Q50/24 , G16H50/20
摘要： An electronic clinical decision support system (CDSS) (10, 12) comprises: an inference engine (20, 22) configured to generate clinical decision recommendations for a patient based on information pertaining to the patient, the inference engine comprising rules (16) developed by a plurality of medical experts (14) and codified into software; an electronic outliers detector (52) configured to detect outlier cases that are probative of a potential flaw in the inference engine; an outliers database (60) configured to collect information pertaining to the outlier cases detected by the electronic outliers detector; and an outliers report generator (62) configured to generate a report (64) on the outlier cases detected by the electronic outliers detector, the generated report containing at least some information collected in the outliers database.
摘要翻译： 电子临床决策支持系统（CDSS）（10,12）包括：推理机（20,22），其被配置为基于与患者相关的信息为患者生成临床决策建议，所述推理机包括规则（16） 由多位医学专家（14）编纂成软件; 电子异常值检测器（52），被配置为检测证明推理机中的潜在缺陷的异常情况; 异常值数据库（60），其被配置为收集与所述电子异常值检测器检测到的异常情况有关的信息; 和异常值报告生成器（62），被配置为生成关于由电子异常值检测器检测到的异常值情况的报告（64），生成的报告包含至少一些在异常值数据库中收集的信息。
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公开(公告)号：US08494871B2
公开(公告)日：2013-07-23
申请号：US12668431
申请日：2008-07-11
申请人： James David Schaffer , Mark R. Simpson , Nicolas Wadih Chbat , Nilanjana Banerjee , Yasser H. Alsafadi
发明人： James David Schaffer , Mark R. Simpson , Nicolas Wadih Chbat , Nilanjana Banerjee , Yasser H. Alsafadi
IPC分类号： G06Q10/00
摘要： A medical apparatus (901, 100) assists clinicians, nurses or other users in choosing an intervention for the treatment of a patent suffering from an acute dynamic disease, e.g. sepsis. The medical apparatus is based on a method where a model of the disease is adapted or personalized to the patient. To ensure that the apparatus remains capable of predicting the health of the patient, the apparatus is continuously provided with new, more recent patient values and the model is continuously adapted to the new patient values. Since the medical apparatus is configured to be continuously adapted to current state of health, the apparatus is able to assist the user by generating disease management information, e.g. suggestions for medications, to an output device (902, 104).
摘要翻译： 医疗器械（901,100）帮助临床医生，护士或其他用户选择治疗患有急性动态疾病的专利的干预措施，例如， 败血症 该医疗装置是基于对患者的疾病模型进行适应或个性化的方法。 为了确保该装置能够预测患者的健康状态，该装置被连续地提供有新的更新的患者值，并且该模型连续地适应于新的患者值。 由于医疗装置被配置为连续地适应当前健康状态，因此该装置能够通过产生疾病管理信息来辅助用户，例如， 对药物的建议，输出设备（902,104）。
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5.发明授权Method and apparatus for automatically developing a high performance classifier for producing medically meaningful descriptors in medical diagnosis imaging 有权用于自动开发用于在医学诊断成像中产生医学上有意义的描述符的高性能分类器的方法和装置公开(公告)号：US08208697B2
公开(公告)日：2012-06-26
申请号：US11721999
申请日：2005-12-13
申请人： James David Schaffer , Walid Ali , Larry J. Eshelman , Claude Cohen-Bacrie , Jean-Michel Lagrange , Claire Levrier , Nicholas Villain , Robert R. Entrekin
发明人： James David Schaffer , Walid Ali , Larry J. Eshelman , Claude Cohen-Bacrie , Jean-Michel Lagrange , Claire Levrier , Nicholas Villain , Robert R. Entrekin
IPC分类号： G06K9/36
CPC分类号： G06K9/6217
摘要： A method for determining the presence or absence of malignant features in medical images, wherein a plurality of base comparison or training images of various types of lesions taken of actual patient is examined by one or more image reading experts to create a first database array. Low-level features of each of the lesions in the same plurality of base comparisons or training images are determined using one or more image processing algorithms to obtain a second database array set. The first and second database array set are combined to create a training database array set which is input to a learning system that discovers/learns a classifier that maps from a subset of the low-level features to the expert's evaluation in the first database array set. The classifier is used to determine the presence of a particular mid-level feature in an image of lesion in a patient based solely on the image.
摘要翻译： 一种用于确定医学图像中恶性特征的存在或不存在的方法，其中由一个或多个图像读取专家检查由实际患者取得的各种类型的损伤的多个基础比较或训练图像以创建第一数据库阵列。 使用一个或多个图像处理算法确定相同多个基本比较或训练图像中的每个病变的低级特征以获得第二数据库阵列集。 组合第一和第二数据库阵列组以创建训练数据库阵列集合，其被输入到发现/学习从低级特征的子集映射到第一数据库阵列集合中的专家评估的分类器的学习系统 。 分类器用于仅基于图像来确定患者的病变图像中特定中级特征的存在。
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公开(公告)号：US08199991B2
公开(公告)日：2012-06-12
申请号：US12516931
申请日：2007-12-03
IPC分类号： G06K9/36
CPC分类号： G06K9/00134 , G06K9/3233 , G06K9/3275 , G06T7/70 , G06T2207/20056 , G06T2207/30072
摘要： A method of automatically identifying the microarray chip corners and probes, even if there are no probes at the corners, in a high density and high resolution microarray scanned image having an image space, wherein the method minimizes the error distortions in the image arising in the scanning process by applying to the image a multipass corner finding algorithm comprising: (a) applying a Radon transform to an input microarray image to project the image into an angle and distance space where it is possible to find the orientation of the straight lines; (b) applying a fast Fourier transform to the projected image of (a) to find the optimal tilting angle of the projected image; (c) determining the optimal first and last local maxima for the optimal tilting angle; (d) back projecting the determined first and last local maxima to the image space to find the first approximation of the first and last column lines of the image; (e) rotating the image and repeating steps (a) through (d) to find the first approximation of the top and bottom row lines of the image; (f) determining the first approximation of the four corners of the image from the intersection of the column and row lines; (g) applying a heuristic for determining if the first approximation of step (f) is sufficient; and (h) optionally trimming the scanned image around the first approximation of the four corners and repeating steps (a) through (f).
摘要翻译： 即使在具有图像空间的高密度和高分辨率的微阵列扫描图像中，即使在角落处没有探针也能够自动识别微阵列芯片角部和探针的方法，其中该方法使图像中产生的图像中的误差失真最小化 扫描过程，通过向图像应用多点角发现算法，包括：（a）将Radon变换应用于输入微阵列图像以将图像投影到可以找到直线的取向的角度和距离空间中; （b）对（a）的投影图像应用快速傅立叶变换以找到投影图像的最佳倾斜角; （c）确定最佳倾斜角的最佳第一和最后局部最大值; （d）将确定的第一和最后局部最大值向前投影到图像空间，以找到图像的第一列和最后一列的第一近似; （e）旋转图像并重复步骤（a）至（d）以找到图像的顶行和下行行的第一近似值; （f）从列和行之间的交点确定图像的四个角的第一近似值; （g）应用启发式来确定步骤（f）的第一近似是否足够; 和（h）可选地修整围绕四个角的第一近似的扫描图像并重复步骤（a）至（f）。
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7.发明授权Detection of errors in the inference engine of a clinical decision support system 有权检测临床决策支持系统推理机中的错误公开(公告)号：US08954339B2
公开(公告)日：2015-02-10
申请号：US12747595
申请日：2008-12-10
申请人： James David Schaffer
发明人： James David Schaffer
CPC分类号： G06F19/345 , G06F19/00 , G06Q50/24 , G16H50/20
摘要： An electronic clinical decision support system (CDSS) (10, 12) comprises: an inference engine (20, 22) configured to generate clinical decision recommendations for a patient based on information pertaining to the patient, the inference engine comprising rules (16) developed by a plurality of medical experts (14) and codified into software; an electronic outliers detector (52) configured to detect outlier cases that are probative of a potential flaw in the inference engine; an outliers database (60) configured to collect information pertaining to the outlier cases detected by the electronic outliers detector; and an outliers report generator (62) configured to generate a report (64) on the outlier cases detected by the electronic outliers detector, the generated report containing at least some information collected in the outliers database.
摘要翻译： 电子临床决策支持系统（CDSS）（10,12）包括：推理机（20,22），其被配置为基于与患者相关的信息为患者生成临床决策建议，所述推理机包括规则（16） 由多位医学专家（14）编纂成软件; 电子异常值检测器（52），被配置为检测证明推理机中的潜在缺陷的异常情况; 异常值数据库（60），其被配置为收集与所述电子异常值检测器检测到的异常情况有关的信息; 和异常值报告生成器（62），被配置为生成关于由电子异常值检测器检测到的异常值情况的报告（64），生成的报告包含至少一些在异常值数据库中收集的信息。
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公开(公告)号：US20100332250A1
公开(公告)日：2010-12-30
申请号：US12747645
申请日：2008-12-10
摘要： A pre-examination patient information gathering system comprises an electronic user interface (30, 130) including a display (32) and at least one user input device (34, 36), and an electronic processor (50) configured to present an initial set of questions (54) to a patient via the electronic user interface, receive responses to the initial set of questions from the patient via the electronic user interface, construct or select follow-up questions (68) based on the received responses, present the constructed or selected follow up questions to the patient via the electronic user interface, and receive responses to the constructed or selected follow up questions from the patient via the electronic user interface. A physiological sensor (70, 72, 74, 76, 78, 80) may be configured to autonomously sense a patient physiological parameter as the patient interacts with the electronic user interface.
摘要翻译： 预检患者信息收集系统包括包括显示器（32）和至少一个用户输入设备（34,36）的电子用户界面（30,130）和被配置为呈现初始设置的电子处理器（50） 通过电子用户界面向患者提供问题（54），经由电子用户界面从患者接收对初始问题集的响应，基于接收到的响应构建或选择后续问题（68），呈现构建的 或经由电子用户界面向患者选择的后续问题，并且经由电子用户界面从患者接收对所构建或选择的跟进问题的响应。 生理传感器（70,72,74,76,78,80）可以被配置为当患者与电子用户界面交互时自主地感测患者生理参数。
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公开(公告)号：US20100008554A1
公开(公告)日：2010-01-14
申请号：US12516931
申请日：2007-12-03
IPC分类号： G06K9/00
CPC分类号： G06K9/00134 , G06K9/3233 , G06K9/3275 , G06T7/70 , G06T2207/20056 , G06T2207/30072
摘要： A method of automatically identifying the microarray chip corners and probes, even if there are no probes at the corners, in a high density and high resolution microarray scanned image having an image space, wherein the method minimizes the error distortions in the image arising in the scanning process by applying to the image a multipass corner finding algorithm comprising: (a) applying a Radon transform to an input microarray image to project the image into an angle and distance space where it is possible to find the orientation of the straight lines; (b) applying a fast Fourier transform to the projected image of (a) to find the optimal tilting angle of the projected image; (c) determining the optimal first and last local maxima for the optimal tilting angle; (d) back projecting the determined first and last local maxima to the image space to find the first approximation of the first and last column lines of the image; (e) rotating the image and repeating steps (a) through (d) to find the first approximation of the top and bottom row lines of the image; (f) determining the first approximation of the four corners of the image from the intersection of the column and row lines; (g) applying a heuristic for determining if the first approximation of step (f) is sufficient; and (h) optionally trimming the scanned image around the first approximation of the four corners and repeating steps (a) through (f).
摘要翻译： 即使在具有图像空间的高密度和高分辨率的微阵列扫描图像中，即使在角落处没有探针也能够自动识别微阵列芯片角部和探针的方法，其中该方法使图像中产生的图像中的误差失真最小化 扫描过程，通过向图像应用多点角发现算法，包括：（a）将Radon变换应用于输入微阵列图像以将图像投影到可以找到直线的取向的角度和距离空间中; （b）对（a）的投影图像应用快速傅立叶变换以找到投影图像的最佳倾斜角; （c）确定最佳倾斜角的最佳第一和最后局部极大值; （d）将确定的第一和最后局部最大值向前投影到图像空间，以找到图像的第一列和最后一列的第一近似; （e）旋转图像并重复步骤（a）至（d）以找到图像的顶行和下行行的第一近似值; （f）从列和行之间的交点确定图像的四个角的第一近似值; （g）应用启发式来确定步骤（f）的第一近似是否足够; 和（h）可选地修整围绕四个角的第一近似的扫描图像并重复步骤（a）至（f）。
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10.发明申请METHOD AND APPARATUS FOR AUTOMATICALLY DEVELOPING A HIGH PERFORMANCE CLASSIFIER FOR PRODUCING MEDICALLY MEANINGFUL DESCRIPTORS IN MEDICAL DIAGNOSIS IMAGING 有权用于自动开发用于在医学诊断成像中生产医学意义描述符的高性能分类器的方法和装置公开(公告)号：US20090268952A1
公开(公告)日：2009-10-29
申请号：US11721999
申请日：2005-12-13
申请人： James David Schaffer , Walid Ali , Larry Eshelman , Claude Cohen-Bacrie , Jean-Michel Lagrange , Claire Levrier , Nicolas Francois Villain , Robert Randall Entrekin
发明人： James David Schaffer , Walid Ali , Larry Eshelman , Claude Cohen-Bacrie , Jean-Michel Lagrange , Claire Levrier , Nicolas Francois Villain , Robert Randall Entrekin
IPC分类号： G06K9/00
CPC分类号： G06K9/6217
摘要： A method for determining the presence or absence of malignant features in medical images, wherein a plurality of base comparison or training images of various types of lesions taken of actual patient is examined by one or more image reading experts to create a first database array. Low-level features of each of the lesions in the same plurality of base comparisons or training images arc determined using one or more image processing algorithms to obtain a second database array set. The first and second database array set are combined to create a training database array set which is input to a learning system that discovers/learns a classifier that maps from a subset of the low-level features to the expert's evaluation in the first database array set. The classifier is used to determine the presence of a particular mid-level feature in an image of lesion in a patient based solely on the image.
摘要翻译： 一种用于确定医学图像中恶性特征的存在或不存在的方法，其中由一个或多个图像读取专家检查由实际患者取得的各种类型的损伤的多个基础比较或训练图像以创建第一数据库阵列。 使用一个或多个图像处理算法确定相同多个基本比较或训练图像中的每个病变的低级特征以获得第二数据库阵列集。 组合第一和第二数据库阵列组以创建训练数据库阵列集合，其被输入到发现/学习从低级特征的子集映射到第一数据库阵列集合中的专家评估的分类器的学习系统 。 分类器用于仅基于图像来确定患者的病变图像中特定中级特征的存在。
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