System and method for personalized treatment prioritization

A system for treatment prioritization, the system comprising:

• one or more processor;
• memory having a treatment prioritization neural network model stored thereon;
• and
• a training module configured to cause the one or more processor to:
• generate an enriched genetic knowledge graph representing relationships between genetic variants, genes, diseases, symptoms, and/or treatments, the enriched genetic knowledge graph comprising vertices and edges, with each vertex corresponding to one of a genetic variant, a gene, a disease, a treatment option, and a symptom;
• analyze the enriched genetic knowledge graph to embed the vertices of the enriched genetic knowledge graph in corresponding genetic knowledge vectors, each genetic knowledge vector encoding information about the corresponding vertex, the corresponding vertex's surrounding graph structure, and/or the corresponding vertex's relationship with other vertices;
• receive a plurality of training patient feature value sets characterizing a plurality of patients;
• generate a plurality of training patient vectors, each training patient vector being generated by scaling the genetic knowledge vectors by one of the plurality of training patient feature value sets;
• generate a training dataset comprising at least a subset of the plurality of training patient vectors, each training patient vector being labelled with a target value comprising an efficacity of at least one treatment option;
• and
• train the treatment prioritization neural network model using the training dataset to process an input patient vector and generate a predicted efficacity of the at least one treatment option.

A system for treatment prioritization, the system comprising:

• one or more processor;
• memory having a trained treatment prioritization neural network model stored thereon;
• an input device configured to receive patient feature values characterizing a patient;
• an inference module configured to cause the one or more processor to:
• generate a patient input vector by scaling genetic knowledge vectors by the patient feature values, and
• provide the patient input vector as an input to the treatment prioritization neural network model, thereby generating a treatment efficacy vector predicting efficacity of a plurality of treatment options;
• and
• an output device configured to convey the predicted efficacity of the plurality of treatment options from a user.