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    Medical Machine Synthetic Data and Corresponding Event Generation
    11.
    发明申请
    Medical Machine Synthetic Data and Corresponding Event Generation 审中-公开

    公开(公告)号：US20200342362A1

    公开(公告)日：2020-10-29

    申请号：US16689798

    申请日：2019-11-20

    Applicant: GE Precision Healthcare LLC

    Inventor： Ravi Soni ,  Min Zhang ,  Gopal B. Avinash ,  Venkata Ratnam Saripalli ,  Jiahui Guan ,  Dibyajyoti Pati ,  Zili Ma

    IPC: G06N20/20 ,  G16H10/60 ,  G06N3/04

    Abstract: Systems, apparatus, instructions, and methods for medical machine time-series event data generation are disclosed. An example synthetic time series data generation apparatus is to generate a synthetic data set including multi-channel time-series data and associated annotation using a first artificial intelligence network model. The example apparatus is to analyze the synthetic data set with respect to a real data set using a second artificial intelligence network model. When the second artificial intelligence network model classifies the synthetic data set as a first classification, the example apparatus is to adjust the first artificial intelligence network model using feedback from the second artificial intelligence network model. When the second artificial intelligence network model classifies the synthetic data set as a second classification, the example apparatus is to output the synthetic data set.

    FASTESTIMATOR HEALTHCARE AI FRAMEWORK
    12.
    发明申请
    FASTESTIMATOR HEALTHCARE AI FRAMEWORK 审中-公开

    公开(公告)号：US20200327379A1

    公开(公告)日：2020-10-15

    申请号：US16699567

    申请日：2019-11-30

    Applicant: GE Precision Healthcare LLC

    Inventor： Xiaomeng Dong ,  Aritra Chowdhury ,  Junpyo Hong ,  Hsi-Ming Chang ,  Gopal B. Avinash ,  Venkata Ratnam Saripalli ,  Karley Yoder ,  Michael Potter

    IPC: G06K9/62 ,  G06N3/08 ,  G06N3/04

    Abstract: An artificial intelligence platform and associated methods of training and use are disclosed. An example apparatus includes a data pipeline to: preprocess data using one or more preprocessing operations applied to features associated with the data; and enable debugging to visualize the preprocessed data. The example apparatus includes a network to: instantiate one or more differentiable operations in a training configuration to train an artificial intelligence model; capture feedback including optimization and loss information to adjust the training configuration; and store one or more metrics to evaluate performance of the artificial intelligence model. The example apparatus includes an estimator to: store the training configuration for the artificial intelligence model; configure the pipeline and the network based on the training configuration; iteratively link the pipeline and the network based on the training configuration; and initiate training of the artificial intelligence model using the linked pipeline and network.

    Determining confident data samples for machine learning models on unseen data
    16.
    发明授权
    Determining confident data samples for machine learning models on unseen data 有权

    公开(公告)号：US11593650B2

    公开(公告)日：2023-02-28

    申请号：US16934650

    申请日：2020-07-21

    Applicant: GE Precision Healthcare LLC

    Inventor： Min Zhang ,  Gopal B. Avinash ,  Zili Ma ,  Kevin H. Leung ,  Wen Jin

    IPC: G06N3/04 ,  G06N3/08 ,  G06F16/28

    Abstract: Techniques are provided for determining confident data samples for machine learning (ML) models on unseen data. In one embodiment, a method is provided that comprises extracting, by a system comprising a processor, a feature vector for a data sample based on projection of the data sample onto a standard feature space. The method further comprises processing, by the system, the feature vector using an outlier detection model to determine whether the data sample is within a scope of a training dataset used to train a machine learning model, wherein the outlier detection model was trained using features extracted from the training dataset based on projection of data samples included in the training dataset onto the standard feature space.

    ANNOTATION PIPELINE FOR MACHINE LEARNING ALGORITHM TRAINING AND OPTIMIZATION
    17.
    发明申请
    ANNOTATION PIPELINE FOR MACHINE LEARNING ALGORITHM TRAINING AND OPTIMIZATION 有权

    公开(公告)号：US20210035015A1

    公开(公告)日：2021-02-04

    申请号：US16528121

    申请日：2019-07-31

    Applicant: GE Precision Healthcare LLC

    Inventor： Marc T. Edgar ,  Travis R. Frosch ,  Gopal B. Avinash ,  Garry M. Whitley

    IPC: G06N20/00 ,  G06N5/04

    Abstract: Techniques are provided for enhancing the efficiency and accuracy of annotating data samples for supervised machine learning algorithms using an advanced annotation pipeline. According to an embodiment, a method can comprise collecting, by a system comprising a processor, unannotated data samples for input to a machine learning model and storing the unannotated data samples in an annotation queue. The method further comprises determining, by the system, annotation priority levels for respective unannotated data samples of the unannotated data samples, selecting, by the system from amongst different annotation techniques, one or more of the different annotation techniques for annotating the respective unannotated data samples based the annotation priority levels associated with the respective unannotated data samples.

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