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    Methods for Explainability of Deep-Learning Models
    1.
    发明申请
    Methods for Explainability of Deep-Learning Models 审中-公开

    公开(公告)号：US20200151516A1

    公开(公告)日：2020-05-14

    申请号：US16680458

    申请日：2019-11-11

    申请人： CurieAI, Inc.

    发明人： Ramin Anushiravani ,  Sridhar Krishna Nemala ,  Ravi Kiran Yalamanchili ,  Navya Swetha Davuluri

    IPC分类号： G06K9/62 ,  G06N3/04 ,  G16H10/60 ,  G16H80/00

    摘要： Embodiments are disclosed for health assessment and diagnosis implemented in an artificial intelligence (AI) system. In an embodiment, a method comprises: feeding a first set of input features to the AI model; obtaining a first set of raw output predictions from the model; determining a first set of impact scores for the input features fed into the model; training a neural network with the first set of impact scores as input to the network and pre-determined sentences describing the model's behavior as output; feeding a second set of input features to the AI model; obtaining a second set of raw output predictions from the model; determining a second set of impact scores based on the second set of output predictions; feeding the second set of impact scores to the neural network; and generating a sentence describing the AI model's behavior on the second set of input features.

    Methods for explainability of deep-learning models
    5.
    发明授权
    Methods for explainability of deep-learning models 有权

    公开(公告)号：US10706329B2

    公开(公告)日：2020-07-07

    申请号：US16680458

    申请日：2019-11-11

    申请人： CurieAI, Inc.

    发明人： Ramin Anushiravani ,  Sridhar Krishna Nemala ,  Ravi Kiran Yalamanchili ,  Navya Swetha Davuluri

    IPC分类号： G06N3/04 ,  G06K9/62 ,  G16H10/60 ,  G16H80/00

    摘要： Embodiments are disclosed for health assessment and diagnosis implemented in an artificial intelligence (AI) system. In an embodiment, a method comprises: feeding a first set of input features to the AI model; obtaining a first set of raw output predictions from the model; determining a first set of impact scores for the input features fed into the model; training a neural network with the first set of impact scores as input to the network and pre-determined sentences describing the model's behavior as output; feeding a second set of input features to the AI model; obtaining a second set of raw output predictions from the model; determining a second set of impact scores based on the second set of output predictions; feeding the second set of impact scores to the neural network; and generating a sentence describing the AI model's behavior on the second set of input features.

    Intelligent Health Monitoring
    7.
    发明申请
    Intelligent Health Monitoring 审中-公开

    公开(公告)号：US20200146623A1

    公开(公告)日：2020-05-14

    申请号：US16680429

    申请日：2019-11-11

    申请人： CurieAI, Inc.

    发明人： Ramin Anushiravani ,  Sridhar Krishna Nemala ,  Ravi Kiran Yalamanchili ,  Navya Swetha Davuluri

    IPC分类号： A61B5/00 ,  A61B5/0205 ,  A61B5/1455 ,  A61B5/11 ,  G16H20/10 ,  G16H70/40 ,  G16H70/60 ,  G16H50/20

    摘要： Embodiments are disclosed for health assessment and diagnosis implemented in an artificial intelligence (AI) system. In an embodiment, a method comprises: capturing, using one or more sensors of a device, signals including information about a user's symptoms; using one or more processors of the device to: collect other data correlative of symptoms experienced by the user; and implement pre-trained data driven methods to: determine one or more symptoms of the user; determine a disease or disease state of the user based on the determined one or more symptoms; determine a medication effectiveness in suppressing at least one determined symptom or improving the determined disease state of the user; and present, using an output device, one or more evidence for at least one of the determined symptoms, the disease, disease state, or an indication of the medication effectiveness for the user.