公开(公告)号：US11900266B2

公开(公告)日：2024-02-13

申请号：US16409473

申请日：2019-05-10

Applicant: MERATIVE US L.P.

Inventor： Murray A. Reicher ,  Stewart Nickolas ,  David Boloker

IPC: G06N5/02 ,  G16H70/20 ,  G16H30/20 ,  G06F9/451 ,  G06N20/00 ,  G06F16/25 ,  G06F16/242 ,  G06F16/435

CPC classification number: G06N5/02 ,  G06F9/453 ,  G06F16/243 ,  G06F16/25 ,  G06F16/437 ,  G06N20/00 ,  G16H30/20 ,  G16H70/20

Abstract: A conversation management system provides a conversational computing interface that manages verbal exchanges between a computer (e.g., artificial intelligence) and a human operator. In a medical embodiment, conversational input from the computing system is generated based on medical knowledge, uses deep learning algorithms, and/or intelligently tracks the state of a conversation so as to the most relevant data to the user.

公开(公告)号：US11875898B2

公开(公告)日：2024-01-16

申请号：US17331251

申请日：2021-05-26

Applicant: MERATIVE US L.P.

Inventor： Luyao Shi ,  David James Beymer ,  Ehsan Dehghan Marvast ,  Deepta Rajan

IPC: G06T7/00 ,  G16H50/20 ,  G16H30/40 ,  G06F18/21

CPC classification number: G16H50/20 ,  G06F18/217 ,  G06T7/0014 ,  G16H30/40 ,  G06T2207/20076 ,  G06T2207/20081 ,  G06T2207/20084 ,  G06T2207/30061 ,  G06T2207/30101

Abstract: Methods and systems for training computer-aided condition detection systems. One method includes receiving a plurality of images for a plurality of patients, some of the images including an annotation associated with a condition; iteratively applying a first deep learning network to each of the images to produce an attention map, a feature map, and an image-level probability of the condition for each of the images; iteratively applying a second deep learning network to each feature map produced by the first network to produce a plurality of outputs; training the first network based on the attention map produced for each image; and training the second network based on the output produced for each of the patients. The second network includes a plurality of convolution layers and a plurality of convolutional long short-term memory (LSTM) layers. Each of the outputs includes a patient-level probability of the condition for one of the patients.

公开(公告)号：US11813113B2

公开(公告)日：2023-11-14

申请号：US17205485

申请日：2021-03-18

Applicant: Merative US L.P.

Inventor： Ehsan Dehghan Marvast ,  Allen Lu ,  Tanveer F. Syeda-Mahmood

IPC: G06T7/00 ,  G06T7/62 ,  A61B8/08 ,  A61B5/00 ,  G16H30/40 ,  A61B5/318 ,  A61B5/316 ,  G06V10/44 ,  G06F18/21 ,  G06F18/25 ,  G06V10/80 ,  G16H10/60

CPC classification number: A61B8/0883 ,  A61B5/316 ,  A61B5/318 ,  A61B5/72 ,  A61B8/5223 ,  G06F18/21 ,  G06F18/253 ,  G06T7/0012 ,  G06T7/0014 ,  G06T7/62 ,  G06V10/454 ,  G06V10/806 ,  G16H30/40 ,  G06T2207/10132 ,  G06T2207/20081 ,  G06T2207/20084 ,  G06T2207/30048 ,  G06V2201/031 ,  G16H10/60

Abstract: Mechanisms are provided to implement an automated echocardiograph measurement extraction system. The automated echocardiograph measurement extraction system receives medical imaging data comprising one or more medical images and inputs the one or more medical images into a deep learning network. The deep learning network automatically processes the one or more medical images to generate an extracted echocardiograph measurement vector output comprising one or more values for echocardiograph measurements extracted from the one or more medical images. The deep learning network outputs the extracted echocardiograph measurement vector output to a medical image viewer.

公开(公告)号：US20230237782A1

公开(公告)日：2023-07-27

申请号：US18158658

申请日：2023-01-24

Applicant: MERATIVE US L.P.

Inventor： Murray A. Reicher ,  Aviad Zlotnick

IPC: G06V10/778 ,  G06F3/04842 ,  G06F18/24 ,  G06F18/40 ,  G06F18/21 ,  G06V30/194

CPC classification number: G06V10/7788 ,  G06F3/04842 ,  G06F18/24 ,  G06F18/41 ,  G06F18/217 ,  G06V30/194 ,  G06V2201/03

Abstract: Systems and techniques are disclosed for improvement of machine learning systems based on enhanced training data. An example method includes providing a visual concurrent display of a set of images of features, the features requiring classification by a reviewing user. The user interface is provided to enable the reviewing user to assign classifications to the images, the user interface being configured to create, read, update, and/or delete classifications. The user interface is responsive to the user, with the user response indicating at least two images with a single classification. The user interface is updated to represent the single classification.

公开(公告)号：US11651243B2

公开(公告)日：2023-05-16

申请号：US15931745

申请日：2020-05-14

Applicant: International Business Machines Corporation

Inventor： Adrian Vrouwenvelder ,  Kimberly Diane Kenna ,  Stephen Alan Carraway ,  John Hefferman

IPC: G06N7/00 ,  G06N5/04 ,  G06N20/00

CPC classification number: G06N5/04 ,  G06N20/00

Abstract: A method, computing platform, and computer program product are provided for evaluating data quality during a clinical trial. A computing platform receives, for a clinical trial, study design information including a set of parameters and corresponding parameter values related to data quality of the clinical trial. During the clinical trial, the computing platform receives query-related information associated with queries from at least some of a plurality of participants of the clinical trial. The computing platform applies the study design information and the query-related information to at least one trained machine learning model to calculate a predicted data quality score indicating data quality for the clinical trial. At least one suggestion for improving the data quality is determined and the predicted data quality score and the at least one suggestion for improving the data quality are output.

公开(公告)号：US12272434B2

公开(公告)日：2025-04-08

申请号：US18221597

申请日：2023-07-13

Applicant: Merative US L.P.

Inventor： Tanveer F. Syeda-Mahmood ,  Chaitanya Shivade

IPC: G16H10/60 ,  G16H15/00 ,  G16H50/70 ,  G16H70/60

Abstract: Mechanisms are provided to implement a patient summary generation engine with deduplication of instances of medical concepts. The patient summary generation engine parses a patient electronic medical record (EMR) to extract a plurality of instances of a medical concept, at least two of which utilize different representations of the medical concept. The patient summary generation engine performs a similarity analysis between each of the instances of a medical concept to thereby calculate, for a plurality of combinations of instances of the medical concept, a similarity metric value. The patient summary generation engine clusters the instances of the medical concept based on the calculated similarity metric values for each combination of instances in the plurality of combinations of instances of the medical concept to thereby generate one or more clusters, and select a representative instance of the medical concept from each cluster in the one or more clusters. The patient summary generation engine generates a summary output of the patient EMR comprising the selected representative instances of the medical concept from each cluster.

公开(公告)号：US12020816B2

公开(公告)日：2024-06-25

申请号：US17483895

申请日：2021-09-24

Applicant: Merative US L.P.

Inventor： Raman Srinivasan ,  Ajay Ashok Deshpande

IPC: G16H50/20 ,  G06F16/28 ,  G16H10/60

CPC classification number: G16H50/20 ,  G06F16/283 ,  G16H10/60

Abstract: A medical episode analysis engine is provided. The engine generates a first matrix data structure having an entry for each concept pairing and storing a value representing relatedness weighted according to a temporal weighting function. The engine generates a second matrix data structure by calculating, for each entry in the first matrix, a relatedness measure of the concepts in the concept pairing based on a frequency of occurrence together. The engine generates, for each first concept, a concept embedding, based on the second matrix, that specifies, for each other second concept, a temporally weighted relatedness measure. The engine generates, for each anchor concepts, a corresponding episode definition comprising a plurality of related concepts corresponding to a same episode, based on the concept embedding. The engine processes new input data based on the episode definition data structures to identify instances of corresponding episodes in the new input data.

公开(公告)号：US20240053307A1

公开(公告)日：2024-02-15

申请号：US18383180

申请日：2023-10-24

Applicant: Merative US L.P.

Inventor： Eric W. Brown ,  Maria Eleftheriou ,  Anca Sailer ,  Ching-Huei Tsou

IPC: G01N30/80 ,  G01N30/32 ,  G01N30/60

CPC classification number: G01N30/80 ,  G01N30/32 ,  G01N30/6073 ,  G01N2030/328 ,  G01N2030/324 ,  G01N2030/326 ,  G01N2030/025

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 a repetitive portion identification and weighting engine. A machine learning model is trained for weighting repetitive portions of patient electronic medical records (EMRs). A repetitive portion identification component applies a plurality of templates to clinical notes of a patient EMR to identify one or more candidate portions that match at least one of the plurality of templates. A content analysis component performs content analysis on the one or more candidate portions to determine whether each given candidate portion is relevant. A weighting component assigns a relative weight to each given candidate portion based on relevance. A cognitive summary graphical user interface (GUI) generation component generates cognitive summary reflecting at least a subset of the one or more candidate portions of the patient EMR. The mechanism outputs the cognitive summary in a GUI to a user.

公开(公告)号：US11823798B2

公开(公告)日：2023-11-21

申请号：US15278875

申请日：2016-09-28

Applicant: Merative US L.P.

Inventor： Corville O. Allen ,  Roberto DeLima ,  Aysu Ezen Can ,  Robert C. Sizemore

IPC: G16H50/20 ,  G16H10/60 ,  G16H70/00 ,  G16H50/70 ,  G06F40/205 ,  G06F40/253 ,  G06F40/284 ,  G06F40/295

CPC classification number: G16H50/20 ,  G06F40/205 ,  G06F40/253 ,  G06F40/284 ,  G06F40/295 ,  G16H10/60 ,  G16H50/70 ,  G16H70/00

Abstract: A mechanism is provided in a data processing system comprising least one processor and at least one memory, the at least one memory comprising instructions executed by the at least one processor to cause the at least one processor to implement a clinical decision support system. The mechanism receives a plurality of patient electronic medical records (EMRs) for a patient from a plurality of different sources. For a portion of a patient EMR record of the plurality of patient EMRs, the mechanism detects entities and analyzes a document structure of the portion of the patient EMR to identify a hierarchical structure of the portion of the patient EMR. The mechanism generates a container representation of the portion of the patient EMR based on the hierarchical structure. The mechanism placing each of the one or more sentences within the container representation based on relative position within the hierarchical structure. The mechanism generates a knowledge graph using the detected entities and the container representation.

公开(公告)号：US20230360751A1

公开(公告)日：2023-11-09

申请号：US18221597

申请日：2023-07-13

Applicant: Merative US L.P.

Inventor： Tanveer F. Syeda-Mahmood ,  Chaitanya Shivade

IPC: G16H10/60 ,  G16H50/70 ,  G16H70/60 ,  G16H15/00

CPC classification number: G16H10/60 ,  G16H50/70 ,  G16H70/60 ,  G16H15/00

Abstract: Mechanisms are provided to implement a patient summary generation engine with deduplication of instances of medical concepts. The patient summary generation engine parses a patient electronic medical record (EMR) to extract a plurality of instances of a medical concept, at least two of which utilize different representations of the medical concept. The patient summary generation engine performs a similarity analysis between each of the instances of a medical concept to thereby calculate, for a plurality of combinations of instances of the medical concept, a similarity metric value. The patient summary generation engine clusters the instances of the medical concept based on the calculated similarity metric values for each combination of instances in the plurality of combinations of instances of the medical concept to thereby generate one or more clusters, and select a representative instance of the medical concept from each cluster in the one or more clusters. The patient summary generation engine generates a summary output of the patient EMR comprising the selected representative instances of the medical concept from each cluster.