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    Leveraging Public Health Data for Prediction and Prevention of Adverse Events
    1.
    发明申请
    Leveraging Public Health Data for Prediction and Prevention of Adverse Events 审中-公开
    利用公共卫生数据预测和预防不良事件

    公开(公告)号：US20140095201A1

    公开(公告)日：2014-04-03

    申请号：US14032522

    申请日：2013-09-20

    申请人： Faisal Farooq ,  Balaji Krishnapuram ,  Glenn Fung ,  Shipeng Yu ,  Karen Nielsen

    发明人： Faisal Farooq ,  Balaji Krishnapuram ,  Glenn Fung ,  Shipeng Yu ,  Karen Nielsen

    IPC分类号： G06F19/00

    CPC分类号： G16H50/30

    摘要： An adverse event may be prevented by predicting the probability of a given patient to have or undergo the adverse event. The ability to predict the probability of the adverse event may be enhanced when a model is derived from public health data to categorize and propose values for medical record fields. The probability alone may prevent the adverse event by educating the patient or medical professional. The probability may be predicted at any time, such as upon entry of information for the patient, periodic analysis, or at the time of admission. The probability may be used to generate a workflow action item to reduce the probability, to warn, to output appropriate instructions, and/or assist in avoiding adverse event. The probability may be specific to a hospital, physician group, or other medical entity, allowing prevention to focus on past adverse event causes for the given entity.

    摘要翻译： 可以通过预测给定患者具有或经历不良事件的可能性来防止不良事件。 当模型从公共卫生数据导出以对医疗记录领域进行分类和建议值时，可以增强预测不良事件概率的能力。 单靠概率可以通过教育患者或医疗专业人员来预防不良事件。 可以随时预测概率，例如在输入患者信息，定期分析或入院时。 概率可以用于生成工作流动作项目以降低概率，警告输出适当的指令和/或协助避免不利事件。 医院，医师团体或其他医疗机构的概率可能是特定的，允许预防集中于给定实体的过去不良事件原因。

    Automated patient/document identification and categorization for medical data
    2.
    发明授权
    Automated patient/document identification and categorization for medical data 有权
    自动病人/文件识别和医疗数据分类

    公开(公告)号：US08751495B2

    公开(公告)日：2014-06-10

    申请号：US12891983

    申请日：2010-09-28

    申请人： Stanley Chung ,  Faisal Farooq ,  Glenn Fung ,  Balaji Krishnapuram ,  R. Bharat Rao ,  Romer E. Rosales ,  John Weis ,  Shipeng Yu

    发明人： Stanley Chung ,  Faisal Farooq ,  Glenn Fung ,  Balaji Krishnapuram ,  R. Bharat Rao ,  Romer E. Rosales ,  John Weis ,  Shipeng Yu

    IPC分类号： G06F7/00 ,  G06F17/00 ,  G06F17/30

    CPC分类号： G06F17/30616 ,  G06F19/00 ,  G16H50/70

    摘要： A method, including receiving a data source selection from a user or software application, the data source including medical information of a plurality of patients, receiving, from the user or software application, a data pattern that is related to a concept to be explored in the data source, querying the data source to find information that approximately matches the data pattern; and receiving the information from the data source, wherein the information includes unstructured data, assigning a classification to individual parts of the information based on the part's relationship to the data pattern, and outputting the classified information to the user or software application.

    摘要翻译： 一种方法，包括从用户或软件应用接收数据源选择，所述数据源包括多个患者的医学信息，从用户或软件应用程序接收与将要探索的概念相关的数据模式 数据源，查询数据源以查找与数据模式近似匹配的信息; 以及从所述数据源接收所述信息，其中，所述信息包括非结构化数据，基于所述部分与所述数据模式的关系，将所述信息的各个部分分配给所述信息，并将所述分类信息输出到所述用户或软件应用。

    Automated Patient/Document Identification and Categorization For Medical Data
    3.
    发明申请
    Automated Patient/Document Identification and Categorization For Medical Data 有权
    医疗数据自动化患者/文件识别和分类

    公开(公告)号：US20110078145A1

    公开(公告)日：2011-03-31

    申请号：US12891983

    申请日：2010-09-28

    申请人： Stanley Chung ,  Faisal Farooq ,  Glenn Fung ,  Balaji Krishnapuram ,  R. Bharat Rao ,  Romer E. Rosales ,  John Weis ,  Shipeng Yu

    发明人： Stanley Chung ,  Faisal Farooq ,  Glenn Fung ,  Balaji Krishnapuram ,  R. Bharat Rao ,  Romer E. Rosales ,  John Weis ,  Shipeng Yu

    IPC分类号： G06F17/30 ,  G06F3/048

    CPC分类号： G06F17/30616 ,  G06F19/00 ,  G16H50/70

    摘要： A method, including receiving a data source selection from a user or software application, the data source including medical information of a plurality of patients, receiving, from the user or software application, a data pattern that is related to a concept to be explored in the data source, querying the data source to find information that approximately matches the data pattern; and receiving the information from the data source, wherein the information includes unstructured data, assigning a classification to individual parts of the information based on the part's relationship to the data pattern, and outputting the classified information to the user or software application.

    摘要翻译： 一种方法，包括从用户或软件应用接收数据源选择，所述数据源包括多个患者的医学信息，从用户或软件应用程序接收与将要探索的概念相关的数据模式 数据源，查询数据源以查找与数据模式近似匹配的信息; 以及从所述数据源接收所述信息，其中，所述信息包括非结构化数据，基于所述部分与所述数据模式的关系，将所述信息的各个部分分配给所述信息，并将所述分类信息输出到所述用户或软件应用。

    Rapid Learning Community for Predictive Models of Medical Knowledge
    7.
    发明申请
    Rapid Learning Community for Predictive Models of Medical Knowledge 审中-公开
    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。

    AUTOMATED MAPPING OF SERVICE CODES IN HEALTHCARE SYSTEMS
    8.
    发明申请
    AUTOMATED MAPPING OF SERVICE CODES IN HEALTHCARE SYSTEMS 审中-公开
    医疗卫生系统服务代码的自动映射

    公开(公告)号：US20140095205A1

    公开(公告)日：2014-04-03

    申请号：US14037548

    申请日：2013-09-26

    申请人： Faisal Farooq ,  Marc Overhage ,  Glenn Fung ,  Shipeng Yu ,  Bharat R. Rao ,  Balaji Krishnapuram ,  Jan DeHaan

    发明人： Faisal Farooq ,  Marc Overhage ,  Glenn Fung ,  Shipeng Yu ,  Bharat R. Rao ,  Balaji Krishnapuram ,  Jan DeHaan

    IPC分类号： G06F19/00

    CPC分类号： G16H10/60

    摘要： Automatic mapping of semantics in healthcare is provided. Data sets have different semantics (e.g., Gender designated with M and F in one system and Sex designated with 1 or 2 in another system). For semantic interoperability, the semantic links between the semantic systems of different healthcare entities are created (e.g., Gender=Sex and/or 1=F and 2=M) by a processor from statistics of the data itself. The distribution of variables, values, or variables and values, with or without other information and/or logic, is used to create a map from one semantic system to another. Similar distributions of other variable and/or values are likely to be for variables and/or values with the same meaning.

    摘要翻译： 提供医疗保健中语义的自动映射。 数据集具有不同的语义（例如，在一个系统中用M和F指定的Gender，在另一个系统中用1或2指定的Sex）。 对于语义互操作性，根据数据本身的统计，处理器创建不同医疗保健实体的语义系统之间的语义链接（例如，Gender = Sex和/或1 = F和2 = M）。 使用或不使用其他信息和/或逻辑的变量，值或变量和值的分布用于创建从一个语义系统到另一个语义系统的映射。 其他变量和/或值的类似分布可能是具有相同含义的变量和/或值。

    Using Candidates Correlation Information During Computer Aided Diagnosis
    9.
    发明申请
    Using Candidates Correlation Information During Computer Aided Diagnosis 有权
    在计算机辅助诊断期间使用候选人相关信息

    公开(公告)号：US20070280530A1

    公开(公告)日：2007-12-06

    申请号：US11742781

    申请日：2007-05-01

    申请人： Glenn Fung ,  Balaji Krishnapuram ,  Volkan Vural ,  R. Rao

    发明人： Glenn Fung ,  Balaji Krishnapuram ,  Volkan Vural ,  R. Rao

    IPC分类号： G06K9/62

    CPC分类号： G06T7/0012 ,  G06K9/6269 ,  G06K9/6278

    摘要： A method and system correlate candidate information and provide batch classification of a number of related candidates. The batch of candidates may be identified from a single data set. There may be internal correlations and/or differences among the candidates. The candidates may be classified taking into consideration the internal correlations and/or differences. The locations and descriptive features of a batch of candidates may be determined. In turn, the locations and/or descriptive features determined may used to enhance the accuracy of the classification of some or all of the candidates within the batch. In one embodiment, the single data set analyzed is associated with an internal image of patient and the distance between candidates is accounted for. Two different algorithms may each simultaneously classify all of the samples within a batch, one being based upon probabilistic analysis and the other upon a mathematical programming approach. Alternate algorithms may be used.

    摘要翻译： 一种方法和系统将候选信息相关联并提供一些相关候选者的批次分类。 可以从单个数据集中识别该批候选。 候选人之间可能存在内部相关性和/或差异。 候选人可以考虑内部相关性和/或差异进行分类。 可以确定一批候选人的位置和描述性特征。 反过来，所确定的位置和/或描述性特征可以用于提高批次内的一些或所有候选者的分类的准确性。 在一个实施例中，所分析的单个数据集与患者的内部图像相关联，并且考虑候选者之间的距离。 两种不同的算法可以各自同时对批次中的所有样本进行分类，一种基于概率分析，另一种基于数学规划方法。 可以使用替代算法。

    System and method for multiple instance learning for computer aided detection
    10.
    发明授权
    System and method for multiple instance learning for computer aided detection 有权
    用于计算机辅助检测的多实例学习的系统和方法

    公开(公告)号：US07986827B2

    公开(公告)日：2011-07-26

    申请号：US11671777

    申请日：2007-02-06

    申请人： R. Bharat Rao ,  Murat Dundar ,  Balaji Krishnapuram ,  Glenn Fung

    发明人： R. Bharat Rao ,  Murat Dundar ,  Balaji Krishnapuram ,  Glenn Fung

    IPC分类号： G06K9/62 ,  G06K9/00 ,  G06E1/00

    CPC分类号： G06K9/6277 ,  G06T7/0012 ,  G06T2207/30004

    摘要： A method of training a classifier for computer aided detection of digitized medical image, includes providing a plurality of bags, each bag containing a plurality of feature samples of a single region-of-interest in a medical image, where each region-of-interest has been labeled as either malignant or healthy. The training uses candidates that are spatially adjacent to each other, modeled by a “bag”, rather than each candidate by itself. A classifier is trained on the plurality of bags of feature samples, subject to the constraint that at least one point in a convex hull of each bag, corresponding to a feature sample, is correctly classified according to the label of the associated region-of-interest, rather than a large set of discrete constraints where at least one instance in each bag has to be correctly classified.

    摘要翻译： 训练用于数字化医学图像的计算机辅助检测的分类器的方法包括提供多个袋，每个袋包含在医学图像中的单个感兴趣区域的多个特征样本，其中每个感兴趣的区域 已被标记为恶性或健康。 培训使用空间上相邻的候选人，由“包”建模，而不是每个候选人本身。 在多个特征样本袋上训练分类器，受限于根据相关区域的标签对每个袋子的凸包中的至少一个点（对应于特征样本）进行正确分类， 而不是大量离散约束，每个行李中的至少一个实例必须被正确分类。