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    System and method for class specific deep learning
    14.
    发明授权
    System and method for class specific deep learning 有权

    公开(公告)号：US11790279B2

    公开(公告)日：2023-10-17

    申请号：US17864694

    申请日：2022-07-14

    Applicant: General Electric Company

    Inventor： Arathi Sreekumari ,  Radhika Madhavan ,  Suresh Emmanuel Joel ,  Hariharan Ravishankar

    IPC: G06N20/20 ,  G06N20/10 ,  G06N3/044 ,  G06N3/047

    CPC classification number: G06N20/20 ,  G06N20/10 ,  G06N3/044 ,  G06N3/047

    Abstract: A method for controlling a physical process includes receiving an input dataset corresponding to the physical process. The method further includes determining a data model based on the input dataset. The data model includes a plurality of latent space variables of a machine learning model. The method also includes receiving a plurality of reference models corresponding to a plurality of classes. Each of the plurality of reference models includes a corresponding plurality of latent space variables. The method includes comparing the data model with each of the plurality of reference models to generate a plurality of distance metric values. The method further includes selecting a reference model among the plurality of reference models based on the plurality of distance metric values. The method also includes controlling the physical process based on the selected reference model.

    PATIENT MONITORING SYSTEM AND METHOD HAVING SEVERITY PREDICTION AND VISUALIZATION FOR A MEDICAL CONDITION
    16.
    发明申请
    PATIENT MONITORING SYSTEM AND METHOD HAVING SEVERITY PREDICTION AND VISUALIZATION FOR A MEDICAL CONDITION 审中-公开

    公开(公告)号：US20200178903A1

    公开(公告)日：2020-06-11

    申请号：US16215128

    申请日：2018-12-10

    Applicant: General Electric Company

    Inventor： Rupanjali Chaudhuri ,  Rohit Pardasani ,  Hariharan Ravishankar ,  Guy Vesto

    IPC: A61B5/00 ,  A61B5/08 ,  A61B5/021 ,  A61B5/0245 ,  A61B5/1455 ,  G06N20/00 ,  G16H50/30

    Abstract: A method of monitoring a patient with respect to a particular medical condition includes providing a machine learning model trained to assign a weight to each of a predefined set of features so as to calculate a risk severity index of a particular medical condition. A long time interval of time-synchronized parameter data is received for each of at least two physiological parameters, and the long time interval is divided into multiple segments each containing a predefined time increment of the parameter data. A set of feature values are determined for the segment based on the parameter data therein, including a feature value for each of the predefined set of features related to the particular medical condition. With the trained machine learning model, assigning a weight to each of the predefined set of features, and then a risk severity index of the particular medical condition is calculated for the long time interval based on the set of feature values.

    METHODS AND SYSTEMS FOR USER DEFINED DISTRIBUTED LEARNING MODELS FOR MEDICAL IMAGING
    18.
    发明申请
    METHODS AND SYSTEMS FOR USER DEFINED DISTRIBUTED LEARNING MODELS FOR MEDICAL IMAGING 审中-公开

    公开(公告)号：US20180157800A1

    公开(公告)日：2018-06-07

    申请号：US15828936

    申请日：2017-12-01

    Applicant: General Electric Company

    Inventor： Hariharan Ravishankar ,  Vivek Vaidya ,  Christian Fritz Perrey

    IPC: G06F19/00

    CPC classification number: G16H50/20 ,  G06F19/321 ,  G06N3/02 ,  G06N20/00 ,  G09B7/00 ,  G09B23/28 ,  G16H10/20 ,  G16H30/40 ,  G16H50/50

    Abstract: Systems and methods are provided for user defined distributed learning models grouped based on user clusters for configuring settings of a medical diagnostic imaging system. The systems and methods are configured to maintain models with predetermined settings for at least one of system settings, image presentation settings, or anatomical structures. The systems and methods are configured to calculate a data value representing select user preferences for a first user, identifying a first cluster based on the data value, and assigning a first model from the models to the first user based on the first cluster. The systems and methods are configured to monitor use of the first model by the first user during a medical diagnostic application to determine whether the first model is updated by the first user or automatically during the medical diagnostic application by changing at least one of system settings, image presentation settings, or anatomical structures.

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