3 research outputs found
Suicidal Ideation and Mental Disorder Detection with Attentive Relation Networks
Mental health is a critical issue in modern society, and mental disorders
could sometimes turn to suicidal ideation without effective treatment. Early
detection of mental disorders and suicidal ideation from social content
provides a potential way for effective social intervention. However,
classifying suicidal ideation and other mental disorders is challenging as they
share similar patterns in language usage and sentimental polarity. This paper
enhances text representation with lexicon-based sentiment scores and latent
topics and proposes using relation networks to detect suicidal ideation and
mental disorders with related risk indicators. The relation module is further
equipped with the attention mechanism to prioritize more critical relational
features. Through experiments on three real-world datasets, our model
outperforms most of its counterparts
Large Language Models in Mental Health Care: a Scoping Review
Objective: The growing use of large language models (LLMs) stimulates a need
for a comprehensive review of their applications and outcomes in mental health
care contexts. This scoping review aims to critically analyze the existing
development and applications of LLMs in mental health care, highlighting their
successes and identifying their challenges and limitations in these specialized
fields. Materials and Methods: A broad literature search was conducted in
November 2023 using six databases (PubMed, Web of Science, Google Scholar,
arXiv, medRxiv, and PsyArXiv) following the 2020 version of the Preferred
Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. A
total of 313 publications were initially identified, and after applying the
study inclusion criteria, 34 publications were selected for the final review.
Results: We identified diverse applications of LLMs in mental health care,
including diagnosis, therapy, patient engagement enhancement, etc. Key
challenges include data availability and reliability, nuanced handling of
mental states, and effective evaluation methods. Despite successes in accuracy
and accessibility improvement, gaps in clinical applicability and ethical
considerations were evident, pointing to the need for robust data, standardized
evaluations, and interdisciplinary collaboration. Conclusion: LLMs show
promising potential in advancing mental health care, with applications in
diagnostics, and patient support. Continued advancements depend on
collaborative, multidisciplinary efforts focused on framework enhancement,
rigorous dataset development, technological refinement, and ethical integration
to ensure the effective and safe application of LLMs in mental health care