| CPC A61N 1/36031 (2017.08) [A61N 1/36034 (2017.08); G16H 10/60 (2018.01); G16H 20/40 (2018.01); A61N 1/0456 (2013.01)] | 20 Claims |
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1. An artificial intelligence (AI)-based system for controlling operations of one or more neuromodulation devices based on one or more physiological parameters of one or more users, comprising:
one or more physiological parameters sensing endpoint devices operatively connected to each neuromodulation device of the one or more neuromodulation devices, configured to determine the one or more physiological parameters of each user of the one or more users;
a connectivity interface configured in each neuromodulation device of the one or more neuromodulation devices to operatively connect with each physiological parameters sensing endpoint device of the one or more physiological parameters sensing endpoint devices for transferring the determined one or more physiological parameters to an associated neuromodulation device of the one or more neuromodulation devices; and
one or more server devices configured with one or more server applications operatively connected to the one or more neuromodulation devices, the one or more server devices comprising:
one or more hardware processors; and
a memory unit operatively connected to the one or more hardware processors, wherein the memory unit comprises a set of computer-readable instructions in form of a plurality of subsystems, configured to be executed by the one or more hardware processors, wherein the plurality of subsystems comprises:
a data-obtaining subsystem configured to obtain at least one of: the one or more physiological parameters from the one or more neuromodulation devices and user-centric data from one or more communication devices associated with each user of one or more users;
a data-processing subsystem configured to process the obtained at least one of: the one or more physiological parameters and the user-centric data using at least one of: one or more artificial intelligence models and one or more machine learning models for deciphering at least one of: multifaceted patterns, correlations, and trends within at least one of: the one or more physiological parameters and the user-centric data;
an operational parameter generation subsystem configured to generate one or more user-centric operational parameters in each neuromodulation device of the one or more neuromodulation devices using at least one of: the one or more artificial intelligence models and the one or more machine learning models, based on the processed at least one of: the one or more physiological parameters and the user-centric data, for optimizing user-centric neuromodulation therapy preferences;
a data recommendation subsystem configured to transmit the generated one or more user-centric operational parameters to the associated neuromodulation device of the one or more neuromodulation devices for controlling operations of the one or more neuromodulation devices;
a learning module configured with an adaptive learning model to continuously optimize at least one of: the one or more artificial intelligence models and the one or more machine learning models based on analyzing real-time at least one of: the one or more physiological parameters and the user-centric data;
a survey module configured to provide a pre-defined set of queries to one or more users during at least one of: a pre-treatment phase and a post-treatment phase for obtaining at least one of: visual analog score (VAS) results, patient global impression of change (PGIC) results, Patient-Reported Outcomes Measurement Information System (PROMIS) score, Oxygen Desaturation Index (ODI), clinical trial data, and real-world data, to continuously update and optimize at least one of: the one or more artificial intelligence models and the one or more machine learning models;
a remote therapy monitoring (RTM) module configured to monitor the real-time efficacy of the neuromodulation therapy by analyzing the obtained user-centric data for generating the one or more user-centric operational parameters; and
a remote patient monitoring (RPM) module configured to analyze obtained at least one of: the one or more physiological parameters and the user-centric data in real-time for altering the one or more user-centric operational parameters in the associated neuromodulation device,
wherein the controlling operations comprise inducing electrical signals through the associated neuromodulation device of the one or more neuromodulation devices, on defined treatment areas on a body of an associated user of the one or more users based on the one or more user-centric operational parameters.
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