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公开(公告)号：US11721441B2
公开(公告)日：2023-08-08
申请号：US16248084
申请日：2019-01-15
Applicant: Merative US L.P.
Inventor： Cheryl L. Eifert , Jia Xu , Claudia S Huettner , Fang Wang , Vanessa Michelini , Elinor Dehan
Abstract: Computer based methods, systems, and computer readable media for intelligently accessing various types of pharmaceutical information in a content repository and ranking drugs at the variant level, gene level, and pathway level. In some cases, drugs that target the same gene, gene variant, or biological pathway may be ranked based upon in vitro, pre-clinical, clinical, or post-clinical evidence. To determine ranking of a plurality of drugs, information pertaining to drug administration is analyzed for the drugs. For a plurality of drugs, attributes corresponding to the drug are determined, wherein the attributes include a variant or a gene targeted by the drug, and a biological pathway comprising the targeted variant or gene. The plurality of drugs are ranked according to a drug effectiveness score based on one or more of a determined efficacy, potency, or toxicity.
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公开(公告)号：US11699508B2
公开(公告)日：2023-07-11
申请号：US16700137
申请日：2019-12-02
Applicant: MERATIVE US L.P.
Inventor： Marina Bendersky , Tanveer Fathima Syeda-Mahmood , Joy Tzung-yu Wu
CPC classification number: G16H15/00 , G06F18/2431 , G06N5/04 , G06N20/00 , G06V10/75 , G16H30/40 , G16H50/70 , G16H70/00
Abstract: Systems and methods for developing a classification model for classifying medical reports, such as radiology reports. One method includes selecting, from a corpus of reports, a training set and a testing set, assigning labels of a modality and an anatomical focus to the reports in both sets, and extracting a sparse representation matrix for each set based on features in the training set. The method also includes learning, with one or more electronic processors, a correlation between the features of the training set and the corresponding labels using a machine learning classifier, thereby building a classification model and testing the classification model on the reports in the testing set for accuracy using the sparse representation matrix of the testing set. The method further includes predicting, with the classification model, labels of an anatomical focus and a modality for remaining reports in the corpus not included in the sets.
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公开(公告)号：US20230215519A1
公开(公告)日：2023-07-06
申请号：US17569467
申请日：2022-01-05
Applicant: MERATIVE US L.P.
Inventor： Tin Kam Ho
Abstract: Methods and systems of determining relevancy of electronic health records to medical analysis objectives. One system includes an electronic processor configured to access electronic health records and extract medical summary data items from the records. The electronic processor is also configured to determine a set of semantic vectors, where each semantic vector represents a medical summary data item. The electronic processor is also configured to determine a set of anatomical semantic vectors. The electronic processor is also configured to determine a similarity score for each medical summary data item. The electronic processor is also configured to receive a medical study and determine a relevancy score for each medical summary data item, the relevancy score representing a relevancy of each medical summary data item to the medical study. The electronic processor is also configured to generate and transmit a notification to a reviewer of the medical study.
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公开(公告)号：US20230206437A1
公开(公告)日：2023-06-29
申请号：US18170789
申请日：2023-02-17
Applicant: MERATIVE US L.P.
Inventor： Hans Harald Zachmann , Simona Rabinovici-Cohen , Shaked Brody
CPC classification number: G06T7/0012 , G06F9/45558 , G06N5/04 , G06F2009/4557 , G06F2009/45575 , G06F2009/45583 , G06T2207/20081
Abstract: Methods and systems for processing medical images. One method includes, in response to startup of an application using an algorithm, creating a server process supporting a programming language associated with the algorithm and loading a plurality of deep learning models used by the algorithm into a memory of the server process to create in-memory models. The method also includes processing a first set of one or more medical images with the server process using the algorithm and at least one model selected from the in-memory models, maintaining the in-memory models in the memory of the server process after processing the first set of one or more medical images, and, in response to a request to process a second set of one or more medical images, processing the second set of one or more medical images using the algorithm and at least one of the in-memory models.
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公开(公告)号：US20230186473A1
公开(公告)日：2023-06-15
申请号：US17948311
申请日：2022-09-20
Applicant: MERATIVE US L.P.
Inventor： Mark D. Bronkalla , James Boritz , John Hansen
CPC classification number: G06T7/0014 , G16H30/20
Abstract: Systems and methods for selecting a prior comparison study. One system includes an electronic processor configured to, for a medical image study associated with a patient, select a prior comparison image study. The electronic processor is also configured to automatically determine, based on monitored user interaction with the selected prior comparison image study, a usefulness of the selected prior comparison image study. The electronic processor is also configured to automatically update a selection model based on the usefulness of the prior comparison image study to a user.
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Patent Agency Ranking
46.发明授权公开(公告)号：US12062456B2
公开(公告)日：2024-08-13
申请号：US17332356
申请日：2021-05-27
Applicant: Merative US L.P.
Inventor： Vishrawas Gopalakrishnan , Ajay Ashok Deshpande , Sayali Navalekar , James H. Kaufman , Simone Bianco , Kun Hu , Xuan Liu , Jacob Ora Miller , Raman Srinivasan , Pan Ding
Abstract: Mechanisms are provided to hypothetical scenario evaluations with regard to infectious disease dynamics based on similar regions. A user definition of a hypothetical scenario for a target region is received which specifies scenario features affecting an infectious disease spread amongst a population within the target region. Other predefined regions, in the set of predefined regions, having similar region characteristics to the target region are identified and attributes of the other predefined regions corresponding to the scenario features are identified. Modified model parameter(s) for an infectious disease computer model are derived based on the identified attributes. An instance of the infectious disease computer model is configured with the modified model parameter(s) and the instance is executed on case report data for the target region to generate a prediction for an infectious disease spread in the target region according to the hypothetical scenario, which is then output.
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公开(公告)号：US11967067B2
公开(公告)日：2024-04-23
申请号：US17319606
申请日：2021-05-13
Applicant: Merative US L.P.
Inventor： Shafiqul Abedin , Hongzhi Wang , Ehsan Dehghan Marvast , David James Beymer
IPC: G06T7/00 , G06F18/2433 , G06N3/045 , G06N3/08 , G06T7/11 , G06V10/34 , G16H30/20 , G16H30/40 , G16H50/20
CPC classification number: G06T7/0012 , G06F18/2433 , G06N3/045 , G06N3/08 , G06T7/11 , G06V10/34 , G16H30/20 , G16H30/40 , G16H50/20 , G06T2207/20081 , G06T2207/20084
Abstract: A candidate generator generates a set of candidate three-dimensional image patches from an input volume. A candidate classifier classifies the set of candidate three-dimensional image patches as containing or not containing disease. Classifying the set of candidate three-dimensional image patches comprises generating an attention mask for each given candidate three-dimensional image patch within the set of candidate three-dimensional image patches to form a set of attention masks, applying the set of attention masks to the set of candidate three-dimensional image patches to form a set of masked image patches, and classifying the set of masked image patches as containing or not containing the disease. The candidate classifier applies soft attention and hard attention to the three-dimensional image patches such that distinctive image regions are highlighted proportionally to their contribution to classification while completely removing image regions that may cause confusion.
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公开(公告)号：US11963790B2
公开(公告)日：2024-04-23
申请号：US16952459
申请日：2020-11-19
Applicant: Merative US L.P.
Inventor： Arkadiusz Sitek , Mark D. Bronkalla , Larissa Christina Schudlo , Benedikt Graf , Yiting Xie
CPC classification number: A61B5/4566 , G06T7/0012 , G06T2207/10081 , G06T2207/10088 , G06T2207/20081 , G06T2207/20084 , G06T2207/30012
Abstract: An approach for a computer program to receive image data of a subject including at least a portion of a spine of the subject and a chronological age of the subject. The approach includes the computer program pre-processing the image data including at least a portion of a spine. The approach includes determining an apparent age of the spine or a portion of the spine of the subject using a trained artificial intelligence deep learning algorithm.
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公开(公告)号：US11948694B2
公开(公告)日：2024-04-02
申请号：US17318027
申请日：2021-05-12
Applicant: Merative US L.P.
Inventor： Vishrawas Gopalakrishnan , Sayali Navalekar , James H. Kaufman , Simone Bianco , Kun Hu , Ajay Ashok Deshpande , Sarah Kefayati , Ujwal Reddy Moramganti , George Sirbu , Xuan Liu , Raman Srinivasan , Pan Ding
Abstract: Mechanisms are provided for compartmental epidemiological computer modeling based on mobility data. Machine learning training of an isolation rate prediction computer model is performed to generate a trained isolation rate prediction model that predicts an isolation rate of a biological population. Isolation data is received which comprises data indicating a measure of mobility of the biological population. The trained isolation rate prediction model is executed on input features extracted from the isolation data to generate a predicted isolation rate. A compartmental epidemiological computer model, comprising a plurality of compartments representing states of a population with regard to an infectious disease, is executed to simulate a progression of the infectious disease and flows of portions of the population from between compartments in the compartmental epidemiological computer model are controlled based on the predicted isolation rate.
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公开(公告)号：US11900601B2
公开(公告)日：2024-02-13
申请号：US18170789
申请日：2023-02-17
Applicant: MERATIVE US L.P.
Inventor： Hans Harald Zachmann , Simona Rabinovici-Cohen , Shaked Brody
CPC classification number: G06T7/0012 , G06F9/45558 , G06N5/04 , G06F2009/4557 , G06F2009/45575 , G06F2009/45583 , G06T2207/20081
Abstract: Methods and systems for processing medical images. One method includes, in response to startup of an application using an algorithm, creating a server process supporting a programming language associated with the algorithm and loading a plurality of deep learning models used by the algorithm into a memory of the server process to create in-memory models. The method also includes processing a first set of one or more medical images with the server process using the algorithm and at least one model selected from the in-memory models, maintaining the in-memory models in the memory of the server process after processing the first set of one or more medical images, and, in response to a request to process a second set of one or more medical images, processing the second set of one or more medical images using the algorithm and at least one of the in-memory models.
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