公开(公告)号：US20240355480A1

公开(公告)日：2024-10-24

申请号：US18587367

申请日：2024-02-26

Applicant: NEUMORA THERAPEUTICS, INC.

Inventor： Yuelu LIU ,  Monika Sharma MELLEM ,  Parvez AHAMMAD ,  Humberto Andres GONZALEZ CABEZAS ,  Matthew KOLLADA

IPC: G16H50/30 ,  A61B5/00 ,  A61B5/055 ,  A61B5/16 ,  G06F18/21 ,  G06F18/214 ,  G06N20/00 ,  G16H10/20 ,  G16H20/70 ,  G16H30/20 ,  G16H30/40 ,  G16H50/20 ,  G16H50/70

CPC classification number: G16H50/30 ,  A61B5/0042 ,  A61B5/055 ,  A61B5/16 ,  A61B5/7267 ,  G06F18/2148 ,  G06F18/2178 ,  G06F18/2193 ,  G06N20/00 ,  G16H10/20 ,  G16H30/20 ,  G16H30/40 ,  G16H50/20 ,  G16H50/70 ,  A61B2576/026 ,  G06V2201/031 ,  G16H20/70

Abstract: A system for evaluating mental health of patients includes a memory and a control system. The memory contains executable code storing instructions for performing a method. The control system is coupled to the memory and includes one or more processors. The control system is configured to execute the machine executable code to cause the control system to perform the method: A selection of answers associated with a patient is received. The selection of answers corresponds to each question in a series of questions from mental health questionnaires. Unprocessed MRI data are received. The unprocessed MRI data correspond to a set of MRI images of a biological structure associated with the patient. The unprocessed MRI data is processed to output a set of MRI features. Using a machine learning model, the selection of answers and the set of MRI features are processed to output a mental health indication of the patient.

公开(公告)号：US20230343463A1

公开(公告)日：2023-10-26

申请号：US18311087

申请日：2023-05-02

Applicant: NEUMORA THERAPEUTICS, INC.

Inventor： Monika Sharma MELLEM ,  Yuelu LIU ,  Parvez AHAMMAD ,  Humberto Andres GONZALEZ CABEZAS ,  William J. MARTIN ,  Pablo Christian GERSBERG

IPC: G16H50/30 ,  G16H50/20 ,  G16H10/60

CPC classification number: G16H50/30 ,  G16H50/20 ,  G16H10/60

Abstract: Systems and methods for utilizing machine learning to generate a trans-diagnostic classifier that is operative to concurrently diagnose a plurality of different mental health disorders using a single trans-diagnostic questionnaire that includes a plurality of questions (e.g., 17 questions). Machine learning techniques are used to process labeled training data to build statistical models that include trans-diagnostic item-level questions as features to create a screen to classify groups of subjects as either healthy or as possibly having a mental health disorder. A subset of questions is selected from the multiple self-administered mental health questionnaires and used to autonomously screen subjects across multiple mental health disorders without physician involvement, optionally remotely and repeatedly, in a short amount of time.

公开(公告)号：US20230343461A1

公开(公告)日：2023-10-26

申请号：US18209866

申请日：2023-06-14

Applicant: NEUMORA THERAPEUTICS, INC.

Inventor： Monika Sharma MELLEM ,  Yuelu Liu ,  Parvez Ahammad ,  Humberto Andres Gonzalez Cabezas ,  William J. Martin ,  Pablo Christian Gersberg

IPC: G16H50/30 ,  G16H50/20 ,  G16H10/60

CPC classification number: G16H50/30 ,  G16H50/20 ,  G16H10/60

Abstract: Systems and methods for utilizing machine learning to generate a trans-diagnostic classifier that is operative to concurrently diagnose a plurality of different mental health disorders using a single trans-diagnostic questionnaire that includes a plurality of questions (e.g., 17 questions). Machine learning techniques are used to process labeled training data to build statistical models that include trans-diagnostic item-level questions as features to create a screen to classify groups of subjects as either healthy or as possibly having a mental health disorder. A subset of questions are selected from the multiple self-administered mental health questionnaires and used to autonomously screen subjects across multiple mental health disorders without physician involvement, optionally remotely and repeatedly, in a short amount of time.