公开(公告)号：US20150227691A1

公开(公告)日：2015-08-13

申请号：US14179752

申请日：2014-02-13

Applicant: Xerox Corporation

Inventor： Sakyajit Bhattacharya ,  Vaibhav Rajan

IPC: G06F19/00 ,  G06N99/00 ,  G06N7/00

CPC classification number: G06N20/00 ,  G06N7/005 ,  G16H10/60 ,  G16H50/20

Abstract: Disclosed are the embodiments for creating a model capable of identifying one or more clusters in a healthcare dataset. An input is received pertaining to a range of numbers. Each number in the range of numbers is representative of a number of clusters in the healthcare dataset. For a cluster, one or more first parameters of a distribution associated with the cluster are estimated. Thereafter, a threshold value is determined based on the one or more first parameters. An inverse cumulative distribution of each of one or more n-dimensional variables in the healthcare dataset is determined. The one or more first parameters are updated to generate one or more second parameters based on the estimated inverse cumulative distribution. A model is created for each number in the range of numbers based on the one or more second parameters.

Abstract translation: 公开了用于创建能够识别保健数据集中的一个或多个聚类的模型的实施例。 接收与数字范围有关的输入。 数字范围内的每个数字代表保健数据集中的多个聚类。 对于集群，估计与集群相关联的分发的一个或多个第一参数。 此后，基于一个或多个第一参数来确定阈值。 确定保健数据集中的一个或多个n维变量中的每一个的逆累积分布。 更新一个或多个第一参数以基于估计的反向累积分布生成一个或多个第二参数。 基于一个或多个第二参数，为数字范围内的每个数字创建模型。

公开(公告)号：US20160306935A1

公开(公告)日：2016-10-20

申请号：US14687128

申请日：2015-04-15

Applicant: XEROX CORPORATION

Inventor： Vaibhav Rajan ,  Sakyajit Bhattacharya

IPC: G06F19/00 ,  A61B5/08 ,  A61B5/021 ,  A61B5/00 ,  A61B5/145

CPC classification number: G16H50/20 ,  A61B5/0022 ,  A61B5/021 ,  A61B5/024 ,  A61B5/082 ,  A61B5/14532 ,  A61B5/7264 ,  A61B5/7275

Abstract: Disclosed are embodiments of methods and systems for predicting a health condition of a first human subject. The method comprises extracting a historical data including physiological parameters of one or more second human subjects. A latent variable is determined based on an inverse cumulative distribution of a transformed historical data, determined by ranking of the historical data. Further, one or more parameters of a first distribution, deterministic of health conditions in the historical data, are determined based on the latent variable. For each physiological parameter, a random variable is sampled from a second distribution of the physiological parameter based on the one or more parameters. Further, based on the random variable, the latent variable is updated. Thereafter, the one or more parameters are re-estimated based on the updated latent variable. Based on the first distribution a classifier is trained to predict the health condition of the first human subject.

Abstract translation: 公开了用于预测第一人类受试者的健康状况的方法和系统的实施例。 该方法包括提取包括一个或多个第二人类受试者的生理参数的历史数据。 潜在变量是根据由历史数据的排序确定的经变换的历史数据的反向累积分布确定的。 此外，基于潜在变量确定历史数据中健康状况的确定性的第一分布的一个或多个参数。 对于每个生理参数，基于一个或多个参数从生理参数的第二分布中采集随机变量。 此外，基于随机变量，更新潜变量。 此后，基于更新的潜在变量重新估计一个或多个参数。 基于第一次分配，训练分类器来预测第一人类受试者的健康状况。

公开(公告)号：US20160300034A1

公开(公告)日：2016-10-13

申请号：US14632117

申请日：2015-02-26

Applicant: XEROX CORPORATION

Inventor： Vijay Huddar ,  Vaibhav Rajan ,  Sakyajit Bhattacharya ,  Shourya Roy

IPC: G06F19/00

CPC classification number: G16H50/30 ,  G16H10/60

Abstract: According to embodiments illustrated herein, there is provided a system for predicting a health condition of a first patient. The system includes a document processor configured to extract one or more headings from one or more medical records of the first patient based on one or more predefined rules. The document processor is further configured to extract one or more words from one or more phrases written under each of the extracted one or more headings, wherein the one or more phrases correspond to documentation of the observation of the first patient by a medical attender. The system further includes one or more processors configured to predict the health condition of the first patient based on a count of the one or more words in historical medical records and the one or more medical records.

Abstract translation: 根据本文所示的实施例，提供了一种用于预测第一患者的健康状况的系统。 该系统包括被配置为基于一个或多个预定规则从第一患者的一个或多个医疗记录提取一个或多个标题的文档处理器。 文档处理器还被配置为从在所提取的一个或多个标题中的每一个标题下写入的一个或多个短语中提取一个或多个单词，其中所述一个或多个短语对应于由医疗人员观察第一患者的文档。 该系统还包括一个或多个处理器，其被配置为基于历史医疗记录中的一个或多个单词的计数和一个或多个医疗记录来预测第一患者的健康状况。

公开(公告)号：US20160246931A1

公开(公告)日：2016-08-25

申请号：US14629766

申请日：2015-02-24

Applicant: XEROX CORPORATION

Inventor： VAIBHAV RAJAN ,  ABHISHEK TRIPATHI ,  Sakyajit Bhattacharya ,  Ranjan Shetty K ,  Amith Sitaram ,  Vivek G. Raman

IPC: G06F19/00 ,  G06N7/00 ,  G06N99/00

CPC classification number: G16H50/20 ,  G06F19/00 ,  G06N7/005 ,  G06N99/005 ,  G16H50/50

Abstract: Disclosed are methods and systems for classifying one or more human subjects in one or more categories indicative of a health condition of the one or more human subjects. The method includes categorizing one or more parameters of each of the one or more human subjects in one or more data views based on a data type of each of the one or more parameters. A data view corresponds to a first data structure storing a set of parameters categorized in the data view, associated with each of the one or more human subjects. The one or more data views are transformed to a second data structure representative of the set of parameters across the one or more data views. Thereafter, a classifier is trained based on the second data structure, wherein the classifier classifies the one or more human subjects in the one or more categories.

Abstract translation: 公开了用于对一个或多个人类受试者进行分类的方法和系统，所述方法和系统指示一种或多种人类受试者的健康状况。 该方法包括基于一个或多个参数中的每一个的数据类型，将一个或多个人物对象中的每一个的一个或多个参数分类为一个或多个数据视图。 数据视图对应于存储与数据视图中分类的一组参数的第一数据结构，其与一个或多个人类对象中的每一个相关联。 所述一个或多个数据视图被转换为代表跨越所述一个或多个数据视图的一组参数的第二数据结构。 此后，基于第二数据结构训练分类器，其中分类器对一个或多个类别中的一个或多个人类对象进行分类。

公开(公告)号：US20170293856A1

公开(公告)日：2017-10-12

申请号：US15093302

申请日：2016-04-07

Applicant: Xerox Corporation

Inventor： Sakyajit Bhattacharya ,  Vaibhav Rajan ,  Asim Anand

IPC: G06N99/00 ,  G06F17/30

CPC classification number: G06F16/285 ,  G06N7/005

Abstract: LASSO constraints can lead to a Gaussian mixture copula model that is more robust, better conditioned, and more reflective of the actual clusters in the training data. These qualities of the GMCM have been shown with data obtained from: digital images of fine needle aspirates of breast tissue for detecting cancer; email for detecting spam; two dimensional terrain data for detecting hills and valleys; and video sequences of hand movements to detect gestures. Using training data, a GMCM estimate can be produced and iteratively refined to maximize a penalized log likelihood estimate until sequential iterations are within a threshold value of one another. The GMCM estimate can then be used to classify further samples. The LASSO constraints help keep the analysis tractibe such that useful results can be found and used while the result is still useful.

公开(公告)号：US20170262597A1

公开(公告)日：2017-09-14

申请号：US15065432

申请日：2016-03-09

Applicant: Xerox Corporation

Inventor： Vijay Huddar ,  Bhupendra Solanki ,  Vaibhav Rajan ,  Sakyajit Bhattacharya

IPC: G06F19/00 ,  G06N99/00

CPC classification number: G16H50/20 ,  G06N20/00

Abstract: A method, non-transitory computer readable medium and apparatus for predicting mortality of a current patient are disclosed. For example, the method includes receiving data associated with a plurality of different patients with known mortality outcomes, wherein the data includes a subset of data for each one of a plurality of different measurement timepoints for each one of the plurality of different patients, calculating n number of classifiers, wherein n is equal to a number of the plurality of different measurement timepoints, receiving data associated with the current patient at an i-th measurement timepoint, predicting the current patient has a high mortality risk based on an output of the i-th classifier of the n number of classifiers and transmitting a signal to a health administration server to cause an alarm to be generated in response to the high mortality risk that is predicted.

公开(公告)号：US20160110315A1

公开(公告)日：2016-04-21

申请号：US14517989

申请日：2014-10-20

Applicant: Xerox Corporation ,  Palo Alto Research Center Incorporated

Inventor： Shailesh Vaya ,  Sakyajit Bhattacharya ,  Sugato Chakrabarty ,  Gregory M. Youngblood ,  Kunal Chawla

IPC: G06F17/21 ,  G06F17/30 ,  G06F17/24

CPC classification number: G06F16/93 ,  G06F17/273 ,  G06F17/277

Abstract: The disclosed embodiments illustrate methods and systems for digitizing a document. The method includes receiving at least one first transcription of content of at least one portion of the document from at least one crowdworker, in response to the at least one portion being crowdsourced as a digitization task to the at least one crowdworker. Thereafter, one or more second transcriptions are determined based on the at least one first transcription. The one or more second transcriptions correspond to intended transcriptions for the at least one portion. Further, the one or more second transcriptions are ranked based at least on a measure of similarity between the at least one first transcription and each of the one or more second transcriptions. At least one second transcription is selected from the one or more second transcriptions as an acceptable transcription for the at least one portion based on the ranking.

Abstract translation: 所公开的实施例示出了用于数字化文档的方法和系统。 所述方法包括响应于所述至少一个部分作为数字化任务向所述至少一个人群工作者响应于从至少一个人群工作者接收所述文档的至少一部分的内容的至少一个第一转录。 此后，基于至少一个第一转录确定一个或多个第二转录。 一个或多个第二转录对应于至少一部分的预期转录。 此外，一个或多个第二转录物至少基于至少一个第一转录和一个或多个第二转录中的每一个之间的相似性的量度进行排名。 从一个或多个第二转录中选择至少一个第二转录作为基于排名的至少一个部分的可接受的转录。

公开(公告)号：US20150302155A1

公开(公告)日：2015-10-22

申请号：US14253941

申请日：2014-04-16

Applicant: Xerox Corporation

Inventor： Vaibhav Rajan ,  Sakyajit Bhattacharya ,  Ranjan Shetty K ,  Amith Sitaram ,  Vivek G. Raman

IPC: G06F19/00

CPC classification number: G16H50/20 ,  G16H50/30

Abstract: Disclosed are the methods and systems for classifying one or more patients in one or more categories. A distribution of one or more physiological parameters associated with the one or more patients is determined based on a patient dataset. The one or more physiological parameters correspond to at least a stroke scale score. One or more parameters associated with a copula are estimated by the one or more processors. In an embodiment, the copula defines a joint distribution of the one or more physiological parameters. A classifier is created based on the one or more parameters, wherein the classifier classifies the one or more patients in the one or more categories. The one or more categories correspond to a range of the stroke scale score.

Abstract translation: 公开了用于对一个或多个类别中的一个或多个患者进行分类的方法和系统。 基于患者数据集确定与一个或多个患者相关联的一个或多个生理参数的分布。 所述一个或多个生理参数对应于至少中风量表得分。 与一个或多个处理器估计与copula相关联的一个或多个参数。 在一个实施例中，所述copula限定所述一个或多个生理参数的联合分布。 基于一个或多个参数创建分类器，其中分类器对一个或多个类别中的一个或多个患者进行分类。 一个或多个类别对应于笔画刻度分数的范围。

公开(公告)号：US20170055916A1

公开(公告)日：2017-03-02

申请号：US14841812

申请日：2015-09-01

Applicant: XEROX CORPORATION

Inventor： Sakyajit Bhattacharya ,  Vaibhav Rajan ,  Harsh Shrivastava

IPC: A61B5/00 ,  A61B5/145 ,  A61B5/0205

CPC classification number: A61B5/7275 ,  A61B5/02055 ,  A61B5/021 ,  A61B5/024 ,  A61B5/0816 ,  A61B5/14542 ,  G16H10/60 ,  G16H50/20 ,  G16H50/30

Abstract: Disclosed are embodiments of methods and systems for predicting mortality of a first patient. The method comprises categorizing a historical data into a first category and a second category. The method further comprises determining a first test parameter and a second test parameter based on at least one of a sample data of a first patient and the historical data corresponding to at least one of the first category and the second category. The method further comprises determining a probability score based on a cumulative distribution of at least one of the first test parameter and the second test parameter. The method further comprises categorizing the sample data in one of the first category and the second category based on the probability score. Further, the method comprises predicting the mortality of the first patient based on at least the categorization of the sample data of the first patient.

Abstract translation: 公开了用于预测第一患者的死亡率的方法和系统的实施例。 该方法包括将历史数据分类为第一类别和第二类别。 该方法还包括基于第一患者的样本数据和对应于第一类别和第二类别中的至少一个的历史数据中的至少一个来确定第一测试参数和第二测试参数。 该方法还包括基于第一测试参数和第二测试参数中的至少一个的累积分布来确定概率得分。 该方法还包括基于概率分数将样本数据分类为第一类别和第二类别之一。 此外，该方法包括至少基于第一患者的样本数据的分类来预测第一患者的死亡率。

公开(公告)号：US20150324695A1

公开(公告)日：2015-11-12

申请号：US14273566

申请日：2014-05-09

Applicant: Xerox Corporation

Inventor： Sakyajit Bhattacharya ,  Tridib Mukherjee ,  Koustuv Dasgupta ,  Shruti Kunde

IPC: G06N5/04 ,  H04L29/08

CPC classification number: G06N5/04 ,  G06N99/005 ,  H04L67/10

Abstract: Methods and systems for creating one or more statistical classifiers. A first set of performance parameters, corresponding to the one or more applications and the one or more computing infrastructures, is extracted from a historical data pertaining to the execution of the one or more applications on the one or more computing infrastructures. Further, a set of application-specific and a set of infrastructure-specific parameters are selected, from the first set of performance parameters, based on one or more statistical techniques. A similarity between each pair of the applications, each pair of the computing infrastructures, and each pair of possible combinations of an application and a computing infrastructure is determined. One or more statistical classifiers are created, based on the determined similarity.

Abstract translation: 用于创建一个或多个统计分类器的方法和系统。 从与一个或多个计算基础设施上的一个或多个应用的​​执行有关的历史数据中提取对应于一个或多个应用和一个或多个计算基础设施的第一组性能参数。 此外，基于一种或多种统计技术，从第一组性能参数中选择一组特定应用和一组基础设施特定参数。 确定每对应用程序，每对计算基础设施以及应用程序和计算基础设施的每对可能组合之间的相似性。 基于所确定的相似度，创建一个或多个统计分类器。