Invention Grant
- Patent Title: Estimating uncertainty in predictions generated by machine learning models
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Application No.: US17960759Application Date: 2022-10-05
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Publication No.: US11887724B2Publication Date: 2024-01-30
- Inventor: Tathagata Banerjee , Matthew Edward Kollada , Amirsina Torfi , Peter Crocker
- Applicant: NEUMORA THERAPEUTICS, INC.
- Applicant Address: US CA San Francisco
- Assignee: Neumora Therapeutics, Inc.
- Current Assignee: Neumora Therapeutics, Inc.
- Current Assignee Address: US CA San Francisco
- Agency: Fish & Richardson P.C.
- Main IPC: G16H50/70
- 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

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.
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