公开(公告)号：US11887724B2

公开(公告)日：2024-01-30

申请号：US17960759

申请日：2022-10-05

Applicant: NEUMORA THERAPEUTICS, INC.

Inventor： Tathagata Banerjee ,  Matthew Edward Kollada ,  Amirsina Torfi ,  Peter Crocker

IPC: G16H50/70 ,  G16H50/20 ,  G06N3/02 ,  G06V10/82 ,  G06V10/774 ,  G06N3/08 ,  G16H30/40 ,  G16H40/20 ,  G06N3/045 ,  G06V10/77 ,  G06T7/00 ,  G06N20/00

CPC classification number: G16H40/20 ,  G06N3/02 ,  G06N3/045 ,  G06N3/08 ,  G06T7/0016 ,  G06V10/774 ,  G06V10/7715 ,  G06V10/82 ,  G16H30/40 ,  G16H50/20 ,  G16H50/70 ,  G06N20/00 ,  G06T2207/10088 ,  G06T2207/10104 ,  G06T2207/20081 ,  G06T2207/20084 ,  G06T2207/30016 ,  G06T2207/30104 ,  G06V2201/03

Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for generating a clinical recommendation for medical treatment of a patient. In one aspect a method comprises: 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; 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.

公开(公告)号：US20230343446A1

公开(公告)日：2023-10-26

申请号：US18212690

申请日：2023-06-21

Applicant: NEUMORA THERAPEUTICS, INC.

Inventor： Tathagata Banerjee ,  Peter Crocker

IPC: G16H30/40 ,  G06N3/02 ,  G06N3/08 ,  G06V10/82 ,  G16H50/70 ,  G06N3/045 ,  G06V10/77 ,  G06T7/00 ,  G16H40/20 ,  G16H50/20 ,  G06V10/774

CPC classification number: G16H40/20 ,  G06N3/02 ,  G06N3/045 ,  G06N3/08 ,  G06T7/0016 ,  G06V10/7715 ,  G06V10/774 ,  G06V10/82 ,  G16H30/40 ,  G16H50/20 ,  G16H50/70 ,  G06N20/00

Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for generating a respective response score for each of a plurality of patient categories. In one aspect, a method comprises: generating a drug signature for a drug; generating an embedding of the drug signature in a latent space; and processing: (i) the embedding of the drug signature in the latent space, and (ii) data defining a plurality of patient categories, to generate a plurality of response scores, wherein each response score corresponds to a respective patient category and characterizes a predicted response of patients included in the patient category to the drug.