Patent ID: 11887724
Assignee: NEUMORA THERAPEUTICS, INC.
Field: Computer technology (Electrical engineering)
Classification: CPC G | IPC G

Claim 0:
1. A method performed by one or more computers, the method comprising:
receiving multi-modal data characterizing a patient, wherein the multi-modal data comprises a respective feature representation for each of a plurality of modalities;
processing the multi-modal data characterizing the patient using a machine learning model, in accordance with values of a set of machine learning model parameters, to generate a patient classification that classifies the patient as being included in a patient category from a set of patient categories, comprising:
processing the multi-modal data characterizing the patient using an encoder neural network of the machine learning model to generate an embedding of the multi-modal data in a latent space; and
generating the patient classification based on the embedding of the multi-modal data in the latent space;

wherein the encoder neural network has been trained on a plurality of training examples by a machine learning training technique, the training comprising, for each training example:
processing training multi-modal data included in the training example using the encoder neural network to generate an embedding of the training multi-modal data;
processing the embedding of the training multi-modal data using a decoder neural network to generate a reconstruction of the training multi-modal data; and
updating current values of a set of encoder neural network parameters using gradients of an objective function that measures an error in the reconstruction of the training multi-modal data;

determining an uncertainty measure that characterizes an uncertainty of the patient classification generated by the machine learning model; and
generating a clinical recommendation for medical treatment of the patient based on: (i) the patient classification, and (ii) the uncertainty measure that characterizes the uncertainty of the patient classification.