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Abstract

Methods and systems for treatment prioritization using a neural network model are provided. Training the model includes generating an enriched genetic knowledge graph with edges characterizing relationships between genetic variants, genes, diseases, treatments and symptoms, embedding the graph vertices in genetic knowledge vectors, receiving training patient feature value sets, generating a training dataset of training patient vectors labelled with target values comprising treatment efficacity by scaling the genetic knowledge vectors by the patient feature value sets, and training the model to process an input patient vector and generate a predicted efficacity of the at least one treatment option. Performing inferences includes receiving patient feature values characterizing a patient, generating the patient input vector by scaling the genetic knowledge vectors by the patient feature values, and providing the patient input vector as input to the model, thereby generating a treatment efficacy vector predicting efficacity of a plurality of treatment options.

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