Invention Grant
- Patent Title: Machine learning-based diagnostic classifier
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Application No.: US16400312Application Date: 2019-05-01
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Publication No.: US11715564B2Publication Date: 2023-08-01
- Inventor: Monika Sharma Mellem , Yuelu Liu , Parvez Ahammad , Humberto Andres Gonzalez Cabezas , William J. Martin , Pablo Christian Gersberg
- Applicant: BlackThorn Therapeutics, Inc.
- Applicant Address: US CA San Francisco
- Assignee: NEUMORA THERAPEUTICS, INC.
- Current Assignee: NEUMORA THERAPEUTICS, INC.
- Current Assignee Address: US CA Brisbane
- Agency: Nixon Peabody LLP
- Main IPC: G16H50/30
- IPC: 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.
Public/Granted literature
- US20190341152A1 MACHINE LEARNING-BASED DIAGNOSTIC CLASSIFIER Public/Granted day:2019-11-07
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