Temporal and context-based transformer neural network for improved communication protocol selection across disparate networks

A system for optimizing and directing electronic communication using transformer-based neural networks, the system comprising a computer-readable medium having a set of non-transitory instructions that when executed by one or more processors, cause the oner or more processors to:

• generate from a set of electronic data sources, a training dataset comprising user profile data, time-stamped behavioral records captured across a plurality of electronic communication channels, external contextual events, and outcome indicators associated with clinical results;
• train a transformer-based neural network with the training dataset by:
• pre-training the transformer-based neural network to learn temporal relationships among events and actions, and
• fine-tuning the pre-trained transformer-based neural network so as to reduce divergence between predicted outcomes and the outcome indicators by optimizing a dual-loss function that (i) applies a generative reconstruction loss causing the transformer-based neural network to predict masked events, and (ii) applies a discriminative outcome loss causing the transformer-based neural network to predict the recorded outcome indicators;
• execute the trained transformer-based neural network using a current user profile data, recent behavioral signals, and contemporaneous external events;
• and
• output an electronic communication instruction associated with the current user profile generated via the trained transformer-based neural network.

A system for optimizing and directing electronic communication using transformer-based neural networks, the system comprising one or more processors configured to:

• generate from a set of electronic data sources, a training dataset comprising user profile data, time-stamped behavioral records captured across a plurality of electronic communication channels, external contextual events, and outcome indicators associated with clinical results;
• train a transformer-based neural network with the training dataset by:
• pre-training the transformer-based neural network to learn temporal relationships among events and actions, and
• fine-tuning the pre-trained transformer-based neural network so as to reduce divergence between predicted outcomes and the outcome indicators by optimizing a dual-loss function that (i) applies a generative reconstruction loss causing the transformer-based neural network to predict masked events, and (ii) applies a discriminative outcome loss causing the transformer-based neural network to predict the recorded outcome indicators;
• execute the trained transformer-based neural network using a current user profile data, recent behavioral signals, and contemporaneous external events;
• and
• output an electronic communication instruction associated with the current user profile generated via the trained transformer-based neural network.