Method And System For AI-Based Generation Of Therapeutic Plans

A method for real-time generation of therapeutic plans based on predictive analytics of patient profile data, comprising:

• receiving, by a therapeutic plan server (TPS) node configured to host a machine learning (ML) module, the patient profile data comprising patient nutrients intake data and medications intake data from the at least one patient-entity node;
• parsing, by the TPS node, configured to host a machine learning (ML) module, the patient profile data to derive a plurality of key classifying features;
• querying, by the TPS node, a local database to retrieve local historical patients-related data based on the plurality of key classifying features;
• generating, by the TPS node, at least one classifier feature vector based on the plurality of key classifying features and the local historical patients-related data;
• providing, by the TPS node, the at least one feature vector to the ML module coupled to an Artificial Neural Network (ANN);
• receiving, by the TPS node, a plurality of nutrients-medications correlation parameters from a therapeutic plan predictive model generated by the ML module using outputs of the ANN based on the at least one feature vector;
• and
• generating, by the TPS node, a therapeutic plan for the at least one patient-entity node based on the nutrients-medications correlation parameters.

A non-transitory computer-readable medium comprising instructions, that when read by a processor, cause the processor to perform:

• receiving the patient profile data comprising patient nutrients intake data and medications intake data from the at least one patient-entity node;
• parsing the patient profile data to derive a plurality of key classifying features;
• querying a local database to retrieve local historical patients-related data based on the plurality of key classifying features;
• generating at least one classifier feature vector based on the plurality of key classifying features and the local historical patients-related data;
• providing the at least one feature vector to a machine learning (ML) module coupled to an Artificial Neural Network (ANN);
• receiving a plurality of nutrients-medications correlation parameters from a therapeutic plan predictive model generated by the ML module using outputs of the ANN based on the at least one feature vector;
• and
• generating a therapeutic plan for the at least one patient-entity node based on the nutrients-medications correlation parameters.