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：US20240221950A1

公开(公告)日：2024-07-04

申请号：US18557873

申请日：2022-04-28

Applicant: NEUMORA THERAPEUTICS, INC.

Inventor： Matthew KOLLADA ,  Tathagata BANERJEE

IPC: G16H50/20 ,  A61B5/00 ,  G16H10/20

CPC classification number: G16H50/20 ,  A61B5/0077 ,  A61B5/4076 ,  A61B5/4842 ,  G16H10/20

Abstract: The disclosed technology is directed to improvements in multi-modal and multi-sensor diagnostic devices, that utilize machine learning algorithms to diagnose patients based on data from different sensor types and formats. Current machine learning algorithms that classify a patient's diagnosis focus on one modality of data output from one type of sensor or device. This is because, among other reasons, it is difficult determine which modalities or features from different modalities will be most important to a diagnosis, and also very difficult to identify an algorithm that can effectively to combine them to diagnose health disorders.