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    Area of Science:

    • Cognitive Psychology
    • Computational Psychiatry
    • Natural Language Processing

    Background:

    • Autobiographical memory is vital for mental health.
    • Depression involves negative memory patterns, impacting future outlook and hopelessness.
    • Current autobiographical memory assessments are subjective and prone to bias.

    Purpose of the Study:

    • To apply Natural Language Processing (NLP) with Large Language Models (LLMs) to objectively analyze autobiographical memory narratives.
    • To determine if the valence of autobiographical memories can predict depression, suicidal ideation, and prior suicide attempts.
    • To explore the correlation between autobiographical memory valence and core components of the Three-Step Theory of suicide, such as hopelessness and connectedness.

    Main Methods:

    • Utilized Large Language Models (LLMs) and Natural Language Processing (NLP) to analyze the valence of autobiographical memory narratives.
    • Correlated memory valence scores with clinical outcomes including depression, suicidal ideation, and suicide attempts.
    • Examined the relationship between memory valence and psychological constructs like hopelessness and connectedness.

    Main Results:

    • Autobiographical memory valence, as analyzed by LLMs, demonstrated predictive accuracy for depression, suicidal ideation, and prior suicide attempts.
    • Memory valence showed a significant correlation with hopelessness and lack of connectedness, key elements of the Three-Step Theory of suicide.
    • The computational approach proved to be a scalable and objective method for suicide risk assessment.

    Conclusions:

    • LLM-based analysis of autobiographical memory offers a novel, objective, and scalable method for psychological assessment.
    • This approach has the potential to significantly enhance suicide risk detection and inform clinical interventions.
    • Findings underscore the utility of advanced computational techniques in advancing mental health care and clinical applications.