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公开(公告)号：US20190261880A1
公开(公告)日：2019-08-29
申请号：US16412533
申请日：2019-05-15
Applicant: International Business Machines Corporation
Inventor： David J. Beymer , Mehdi Moradi , Mohammadreza Negahdar , Nripesh Parajuli , Tanveer F. Syeda-Mahmood
IPC: A61B5/04 , A61B8/08 , A61B5/00 , A61B5/0402
Abstract: An automatic extraction of disease-specific features from Doppler images to help diagnose valvular diseases is provided. The method includes the steps of obtaining a raw Doppler image from a series of images of an echocardiogram, isolating a region of interest from the raw Doppler image, the region of interest including a Doppler image and an ECG signal, and depicting at least one heart cycle, determining a velocity envelope of the Doppler image in the region of interest, extracting the ECG signal to synchronize the ECG signal with the Doppler image over the at least one heart cycle, within the region of interest, calculating a value of a clinical feature based on the extracted ECG signal synchronized with the velocity envelope, and comparing the value of the clinical feature with clinical guidelines associated with the clinical feature to determine a diagnosis of a disease.
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公开(公告)号：US20150278707A1
公开(公告)日：2015-10-01
申请号：US14230400
申请日：2014-03-31
Applicant: International Business Machines Corporation
Inventor： David J. Beymer , Karen W. Brannon , Ting Chen , Ritwik K. Kumar , Tanveer Syeda-Mahmood
CPC classification number: G06N20/00
Abstract: Embodiments relate to creating a classification rule by combining classifiers. Aspects include receiving N training samples d, wherein each of the N training samples d includes a label l, receiving T classifiers C, and initializing a first random weight vector α for the N training samples d. Aspects also include initializing a second random weight vector β for the T classifiers C and creating, by a processor, the classification rule by identifying a combination of one or more of the T classifiers C that best approximates the label l for each of the N training samples d based on the first random weight vector and the second random weight vector β.
Abstract translation: 实施例涉及通过组合分类器来创建分类规则。 方面包括接收N个训练样本d，其中N个训练样本d中的每一个包括标签l，接收T分类器C，以及初始化N个训练样本d的第一随机加权矢量α。 方面还包括初始化第二随机权重向量＆bgr; 对于T分类器C，并且由处理器通过基于第一随机权重向量识别对N个训练样本d中的每一个最接近标签l的一个或多个T分类器C的组合来创建分类规则;以及 第二随机权重向量＆bgr。
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公开(公告)号：US20150098638A1
公开(公告)日：2015-04-09
申请号：US14045869
申请日：2013-10-04
Applicant: International Business Machines Corporation
Inventor： Tanveer F. Syeda-Mahmood , Fei Wang , David J. Beymer
CPC classification number: G06F17/30256 , G06K9/00 , G06K9/4671 , G06K9/6215 , G06K2209/051
Abstract: Embodiments relate to finding similar coronary angiograms in a database of coronary angiograms. An aspect includes receiving angiography data for a coronary artery, processing the angiography data to identify one or more semantic features of the coronary artery, and identifying one or more nearest coronary angiograms for each of the one or more semantic features from the database of coronary angiograms. The method also includes receiving a disease attribute associated with each of the one or more nearest coronary angiograms.
Abstract translation: 实施例涉及在冠状动脉造影图数据库中发现类似的冠状动脉造影。 一个方面包括接收冠状动脉的血管造影数据，处理血管造影数据以识别冠状动脉的一个或多个语义特征，以及从冠状动脉造影图数据库中识别一个或多个语义特征中的每一个的一个或多个最近冠状动脉造影图 。 该方法还包括接收与一个或多个最近的冠状动脉造影图中的每一个相关联的疾病属性。
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公开(公告)号：US10362949B2
公开(公告)日：2019-07-30
申请号：US15295389
申请日：2016-10-17
Applicant: International Business Machines Corporation
Inventor： David J. Beymer , Mehdi Moradi , Mohammadreza Negahdar , Nripesh Parajuli , Tanveer F. Syeda-Mahmood
Abstract: An automatic extraction of disease-specific features from Doppler images to help diagnose valvular diseases is provided. The method includes the steps of obtaining a raw Doppler image from a series of images of an echocardiogram, isolating a region of interest from the raw Doppler image, the region of interest including a Doppler image and an ECG signal, and depicting at least one heart cycle, determining a velocity envelope of the Doppler image in the region of interest, extracting the ECG signal to synchronize the ECG signal with the Doppler age over the at least one heart cycle, within the region of interest, calculating a value of a clinical feature based on the extracted ECG signal synchronized with the velocity envelope, and comparing the value of the clinical feature with clinical guidelines associated with the clinical feature to determine a diagnosis of a disease.
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5.发明申请公开(公告)号：US20190197135A1
公开(公告)日：2019-06-27
申请号：US15855454
申请日：2017-12-27
Applicant: International Business Machines Corporation
Inventor： David J. Beymer , Ehsan Dehghan Marvast , Ahmed El Harouni , Girish Narayan , Tanveer F. Syeda-Mahmood
CPC classification number: G06F16/54 , G06F3/0482 , G06F16/51 , G06F16/532 , G06T7/0012 , G06T2200/24 , G16H30/40
Abstract: A mechanism is provided in a data processing system comprising a processor and a memory, the memory comprising instructions that are executed by the processor to specifically configure the processor to implement an intelligent medical image viewing engine. The intelligent medical image viewing engine receives a medical imaging study data structure comprising a plurality of electronic medical images from a medical image database. An image processing component executing within the intelligent medical image viewing engine analyzes the medical imaging study data structure to identify, for each electronic medical image in the plurality of electronic medical images, a corresponding set of image attributes. The intelligent medical image viewing engine receives a user input specifying at least one filter attribute for generating a medical image output and correlates the at least one filter attribute with at least one medical attribute of medical images to be used for selection of electronic medical images from the medical imaging study data structure. An image selection component executing within the intelligent medical image viewing engine selects a subset of the electronic medical images in the plurality of electronic medical images based on the correlation of the at least one filter attribute with the at least one medical attribute. A user interface generation component executing within the intelligent medical image viewing engine generates and outputs a medical image output comprising the subset of electronic medical images based on the selection.
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Patent Agency Ranking
公开(公告)号：US10327712B2
公开(公告)日：2019-06-25
申请号：US14540159
申请日：2014-11-13
Applicant: International Business Machines Corporation
Inventor： David J. Beymer , Karen W. Brannon , Colin B. Compas , Ritwik K. Kumar , Tanveer F. Syeda-Mahmood
Abstract: Use of medical workflows where a first medical workflow is obtained from a plurality of medical acts performed in sequence that related to care of a patient. A set of condition-indication rules is applied to the first medical workflow to determine first condition information. The first condition information relates to a likelihood that a first medical condition exists in the patient.
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公开(公告)号：US09349105B2
公开(公告)日：2016-05-24
申请号：US14133193
申请日：2013-12-18
Applicant: International Business Machines Corporation
Inventor： David J. Beymer , Karen W. Brannon , Ting Chen , Moritz A. W. Hardt , Ritwik K. Kumar , Tanveer F. Syeda-Mahmood
CPC classification number: G06N99/005
Abstract: Machine learning solutions compensate for data missing from input (training) data and thereby arrive at a predictive model that is based upon, and consistent with, the training data. The predictive model can be generated within a learning algorithm framework by transforming the training data to generate modality or similarity kernels. Similarity values can be generated for these missing similarity values.
Abstract translation: 机器学习解决方案可以补偿输入（训练）数据丢失的数据，从而达到基于并与训练数据一致的预测模型。 可以通过将训练数据转换为生成模态或相似性内核，在学习算法框架内生成预测模型。 可以为这些缺失的相似度值生成相似度值。
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公开(公告)号：US09135272B2
公开(公告)日：2015-09-15
申请号：US14045869
申请日：2013-10-04
Applicant: International Business Machines Corporation
Inventor： Tanveer F. Syeda-Mahmood , Fei Wang , David J. Beymer
CPC classification number: G06F17/30256 , G06K9/00 , G06K9/4671 , G06K9/6215 , G06K2209/051
Abstract: Embodiments relate to finding similar coronary angiograms in a database of coronary angiograms. An aspect includes receiving angiography data for a coronary artery, processing the angiography data to identify one or more semantic features of the coronary artery, and identifying one or more nearest coronary angiograms for each of the one or more semantic features from the database of coronary angiograms. The method also includes receiving a disease attribute associated with each of the one or more nearest coronary angiograms.
Abstract translation: 实施例涉及在冠状动脉造影图数据库中发现类似的冠状动脉造影。 一方面包括接收用于冠状动脉的血管造影数据，处理血管造影数据以识别冠状动脉的一个或多个语义特征，以及从冠状动脉造影图数据库中识别一个或多个语义特征中的每一个的一个或多个最近冠状动脉造影图 。 该方法还包括接收与一个或多个最近的冠状动脉造影图中的每一个相关联的疾病属性。
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公开(公告)号：US09092849B2
公开(公告)日：2015-07-28
申请号：US13930465
申请日：2013-06-28
Applicant: International Business Machines Corporation
Inventor： David J. Beymer , Ritwik K. Kumar , Tanveer F. Syeda-Mahmood , Fei Wang , Yong Zhang
CPC classification number: G06T7/0012 , G06T5/002 , G06T7/11 , G06T7/194 , G06T2207/30101
Abstract: Embodiments relate to segmenting blood vessels in angiogram images. An aspect includes a method that includes receiving and preprocessing at least one angiogram frame and preprocessing. In one embodiment, at least one angiogram frame is received and preprocessed. Bottom-up filtering of the preprocessed angiogram frame and top-down segmentation of the preprocessed angiogram frame are performed based on the results of the bottom-up filtering. The bottom-up filtering and the top-down segmentation are iteratively repeated until the difference between results of the top-down segmentation from consecutive iterations is equal to or below a threshold value. Based on determining that a difference between results of the top-down segmentation from consecutive iterations is below or equal to the threshold value, the results of the top-down segmentation are outputted.
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公开(公告)号：US11990216B2
公开(公告)日：2024-05-21
申请号：US18070615
申请日：2022-11-29
Applicant: International Business Machines Corporation
Inventor： Yufan Guo , David J. Beymer , Tyler Baldwin , Vandana Mukherjee , Tanveer F. Syeda-Mahmood
IPC: G16H10/60 , G06F16/335
CPC classification number: G16H10/60 , G06F16/335
Abstract: A mechanism is provided for implement a discrepancy detection mechanism for detecting discrepancies between clinical notes and administrative records. Clinical concepts are extracted from the clinical notes and the administrative records in a patient's electronic medical records (EMRs). The extracted clinical concepts are filtered based on semantic type information to identify concepts that reference diseases or syndromes while also removing negated instances. Utilizing the positive mentions of diseases in clinical notes, the positive mentions of diseases or syndromes in the clinical notes are compared against each positive entry in the administrative records. A discrepancy summary is then generated for diseases or syndromes that failed to translate correctly from clinical notes to the administrative records in the patient's EMRs.
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