US 11,942,224 B2
System and method for identifying transdiagnostic features shared across mental health disorders
Yuelu Liu, San Francisco, CA (US); Monika Sharma Mellem, San Francisco, CA (US); Parvez Ahammad, San Francisco, CA (US); Humberto Andres Gonzalez Cabezas, San Francisco, CA (US); and Matthew Kollada, San Francisco, CA (US)
Assigned to NEUMORA THERAPEUTICS, INC., Brisbane, CA (US)
Filed by Blackthorn Therapeutics, Inc., San Francisco, CA (US)
Filed on Jan. 3, 2022, as Appl. No. 17/646,756.
Application 17/646,756 is a continuation of application No. 17/270,730, granted, now 11,244,762, previously published as PCT/US2019/048762, filed on Aug. 29, 2019.
Claims priority of provisional application 62/725,994, filed on Aug. 31, 2018.
Prior Publication US 2022/0139560 A1, May 5, 2022
This patent is subject to a terminal disclaimer.
Int. Cl. G16H 50/30 (2018.01); A61B 5/00 (2006.01); A61B 5/055 (2006.01); A61B 5/16 (2006.01); G06F 18/21 (2023.01); G06F 18/214 (2023.01); G06N 20/00 (2019.01); G16H 10/20 (2018.01); G16H 30/20 (2018.01); G16H 30/40 (2018.01); G16H 50/20 (2018.01); G16H 50/70 (2018.01); G16H 20/70 (2018.01)
CPC G16H 50/30 (2018.01) [A61B 5/0042 (2013.01); A61B 5/055 (2013.01); A61B 5/16 (2013.01); A61B 5/7267 (2013.01); G06F 18/2148 (2023.01); G06F 18/2178 (2023.01); G06F 18/2193 (2023.01); G06N 20/00 (2019.01); G16H 10/20 (2018.01); G16H 30/20 (2018.01); G16H 30/40 (2018.01); G16H 50/20 (2018.01); G16H 50/70 (2018.01); A61B 2576/026 (2013.01); G06V 2201/031 (2022.01); G16H 20/70 (2018.01)] 20 Claims
OG exemplary drawing
 
1. A system for evaluating a patient for mental health issues, the system comprising:
a user interface;
a memory containing machine readable medium comprising machine executable code having stored thereon instructions for performing a method; and
a control system coupled to the memory comprising one or more processors, the control system configured to execute the machine executable code to cause the control system to:
receive, from the user interface, a selection of answers from a patient, the selection of answers corresponding to each question in a series of questions from mental health questionnaires;
receive, unprocessed MM data associated with the patient; and
process, using a machine learning model, the selection of answers and the unprocessed MRI data to output a mental health indication of the patient,
wherein the machine learning model was generated by:
receiving training data corresponding to a plurality of individuals, the training data comprising:
MRI data; and
a selection of answers to the series of questions;
determining a plurality of features from the training data;
extracting importance measures for each of the plurality of features;
generating a plurality of subset machine learning models based on the extracted importance measures for the plurality of features; and
selecting at least one of the subset machine learning models as the machine learning model.