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.

Abstract: An input dataset is processed to obtain a pre-event window of model inputs and a post-event window of model inputs. The input dataset is a subset of a larger subject-patient dataset that includes different data types and features of the patient. The data types are based on electrical activity of the patient's brain that is sensed and stored by an implanted neurostimulation system. A clinical response estimator (CRE) model is applied to the pre-event window of model inputs to derive pre-event CRE biomarkers. The CRE model is also applied to the post-event window of model inputs to derive post-event CRE biomarkers. The pre-event CRE biomarkers and post-event CRE biomarkers are displayed as a function of time together with the occurrence of the event of interest.

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.

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.

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.

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.