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    SYSTEMS AND METHODS FOR ASSESSING THE EFFECTIVENESS OF A THERAPY INCLUDING A DRUG REGIMEN USING AN IMPLANTABLE MEDICAL DEVICE
    2.
    发明申请
    SYSTEMS AND METHODS FOR ASSESSING THE EFFECTIVENESS OF A THERAPY INCLUDING A DRUG REGIMEN USING AN IMPLANTABLE MEDICAL DEVICE 有权

    公开(公告)号：US20210298654A1

    公开(公告)日：2021-09-30

    申请号：US17346944

    申请日：2021-06-14

    Applicant: NeuroPace, Inc.

    Inventor： Tara L. Crowder ,  Brett M. Wingeier ,  Martha J. Morrell ,  Felice Sun ,  Thomas Tcheng

    IPC: A61B5/24 ,  A61B5/00 ,  A61B5/377

    Abstract: Systems and methods rely on feedback from an active medical device or devices (e.g., neurostimulator coupled to sensing and stimulation elements such as electrodes) to assess the effectiveness of a patient's drug regimen. Such reliance may include analyzing characteristics in physiological data acquired by the medical device(s), for example, in the form of responses evoked from the patient by electrical stimulation waveforms. Systems and methods further involved adjusting one or more parameters according to which a combination therapy consisting of at least a drug regimen and an electrical stimulation therapy are delivered to a patient, in an effort to optimize the therapeutic effect of the combination. The adjustments may be automatically by one or more implanted or external hosts working together or alone, and/or with the input of a physician.

    SYSTEMS AND METHODS FOR OBTAINING A CLINICAL RESPONSE ESTIMATE BIOMARKER USING MACHINE-LEARNED MODELS TRAINED ON IMPLANTED NEUROSTIMULATOR DATA
    5.
    发明申请
    SYSTEMS AND METHODS FOR OBTAINING A CLINICAL RESPONSE ESTIMATE BIOMARKER USING MACHINE-LEARNED MODELS TRAINED ON IMPLANTED NEUROSTIMULATOR DATA 有权

    公开(公告)号：US20220323019A1

    公开(公告)日：2022-10-13

    申请号：US17709159

    申请日：2022-03-30

    Applicant: NeuroPace, Inc.

    Inventor： Sharanya Arcot Desai ,  Tara L. Crowder ,  Thomas K. Tcheng ,  Martha J. Morrell ,  Lise Johnson Dyhr

    IPC: A61B5/00 ,  G06N20/20

    Abstract: A clinical response estimate (CRE) biomarker of a patient having an implanted neurostimulation system is monitored. To this end, an input dataset is derived from a subject-patient dataset that includes various different data types and different features of the patient. The data types are based on electrical activity the patient's brain sensed and stored by the implanted neurostimulation system. The input dataset is a subset of the larger subject-patient dataset, and the specific data types and patient features included in that subset are derived based on a plurality of key inputs of the subject-patient dataset. Once the input dataset is derived, it is processed by a clinical response estimator having machine-learned models. First and second machine-learned models of the clinical response estimator are applied to the input dataset to provide model inputs to an ensemble machine-learned model to determine the CRE biomarker.

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