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公开(公告)号：US20230214994A1
公开(公告)日：2023-07-06
申请号：US17569465
申请日：2022-01-05
Applicant: MERATIVE US L.P.
Inventor： Sun Young Park , Dustin Michael Sargent , Benedikt Graf , Larissa Christina Schudlo , Marwan Sati
CPC classification number: G06T7/0012 , G16H50/20 , G06F40/20 , G16H30/40 , G06T2207/20081
Abstract: Methods and systems for assigning a medical image study for review. One method includes receiving a plurality of labeled medical image studies and one or more prior image studies of a patient associated with each of plurality of labeled medical image studies. The method also includes creating a set of training data including the plurality of labeled medical image studies and the one or more prior image studies received for each of the plurality of labeled medical image studies and training an artificial intelligence (AI) system using the set of training data. In addition, the method includes estimating, using the AI system as trained, a difficulty metric for an unlabeled medical image study based on the unlabeled medical image study and one or more prior image studies of a patient associated with the unlabeled image study and assigning the unlabeled medical image study for review based on the difficulty metric.
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公开(公告)号：US11645833B2
公开(公告)日：2023-05-09
申请号：US17528566
申请日：2021-11-17
Applicant: MERATIVE US L.P.
Inventor： Ali Madani , Mehdi Moradi , Tanveer F. Syeda-Mahmood
IPC: G16H30/40 , G16H30/20 , G06T7/00 , A61B6/00 , G06N5/02 , G06N20/00 , G06N3/08 , G06N3/04 , G06K9/62 , G06N5/022 , G06N3/082
CPC classification number: G06K9/6259 , A61B6/5217 , G06K9/6267 , G06N3/0454 , G06N3/0472 , G06N3/0481 , G06N3/082 , G06N5/022 , G06N20/00 , G06T7/0014 , G16H30/20 , G16H30/40 , G06T2207/10116 , G06T2207/30048
Abstract: Mechanisms are provided to implement a machine learning training model. The machine learning training model trains an image generator of a generative adversarial network (GAN) to generate medical images approximating actual medical images. The machine learning training model augments a set of training medical images to include one or more generated medical images generated by the image generator of the GAN. The machine learning training model trains a machine learning model based on the augmented set of training medical images to identify anomalies in medical images. The trained machine learning model is applied to new medical image inputs to classify the medical images as having an anomaly or not.
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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
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.
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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
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.
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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.
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Patent Agency Ranking
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
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.
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公开(公告)号：US20230360745A1
公开(公告)日：2023-11-09
申请号：US18223082
申请日：2023-07-18
Applicant: Merative US L.P.
IPC: G16H10/00 , G06F40/205 , G16H70/00 , G06F40/174 , G06F40/55 , G06F40/247
CPC classification number: G16H10/00 , G06F40/205 , G16H70/00 , G06F40/174 , G06F40/55 , G06F40/247
Abstract: A system and related method exchange medical information with a medical management system. The method comprises receiving, using a processor of a code point resolver, from the medical management system, medical text via a network interface. A code point is a single standardized medical terminology code (SMTC) that corresponds to a medical concept contained within the medical text. The method further applies rule-based logic to process the medical text to form a localized mapping of a text portion of the medical text to a plurality of candidate SMTCs (CSMTCs) that are related to at least one metathesaurus concept entity (MCE) in a metathesaurus, and to determines the code point from the CSMTCs. The method transmits, via the network interface, to the medical management system, the code point.
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公开(公告)号：US11763081B2
公开(公告)日：2023-09-19
申请号：US17061628
申请日：2020-10-02
Applicant: Merative US L.P.
Inventor： Tanveer Syeda-Mahmood
IPC: G06F40/284 , G16H30/20 , G16H30/40 , G16H70/20 , G16H70/60 , G06F40/247 , G16H50/20 , G06F16/2455 , G06F40/205 , G06F40/289 , G16H50/70
CPC classification number: G06F40/284 , G06F16/24558 , G06F40/205 , G06F40/247 , G06F40/289 , G16H30/20 , G16H30/40 , G16H50/20 , G16H50/70 , G16H70/20 , G16H70/60
Abstract: Mechanisms are provided to implement a fine-grained finding descriptor generation computing tool that automatically generates fine-grained labels for downstream computer system operations. The mechanisms process medical report content based on a core finding lexicon, to extract core finding instances from the medical report content. The mechanisms execute, for each core finding instance, automated computer NLP operations that generate a parse tree for the portion of the medical report content corresponding to the core finding instance, perform phrasal grouping on the parse tree to thereby associate one or more modifiers of core findings specified in the portion of the medical report content with the core finding instance, and generate a fine-grained finding descriptor data structure for the core finding instance based on the association of one or more modifiers of the core finding with the core finding instance.
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公开(公告)号：US11749387B2
公开(公告)日：2023-09-05
申请号：US17350441
申请日：2021-06-17
Applicant: Merative US L.P.
Inventor： Tanveer F. Syeda-Mahmood , Chaitanya Shivade
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.
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公开(公告)号：US11749384B2
公开(公告)日：2023-09-05
申请号：US17161828
申请日：2021-01-29
Applicant: Merative US L.P.
IPC: G16H10/00 , G06F40/205 , G16H70/00 , G06F40/174 , G06F40/55 , G06F40/247
CPC classification number: G16H10/00 , G06F40/174 , G06F40/205 , G06F40/247 , G06F40/55 , G16H70/00
Abstract: A system and related method exchange medical information with a medical management system. The method comprises receiving, using a processor of a code point resolver, from the medical management system, medical text via a network interface. A code point is a single standardized medical terminology code (SMTC) that corresponds to a medical concept contained within the medical text. The method further applies rule-based logic to process the medical text to form a localized mapping of a text portion of the medical text to a plurality of candidate SMTCs (CSMTCs) that are related to at least one metathesaurus concept entity (MCE) in a metathesaurus, and to determines the code point from the CSMTCs. The method transmits, via the network interface, to the medical management system, the code point.
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