Latest AI and machine learning research in psychiatry for healthcare professionals.
Automating the generation of Planning Domain Definition Language (PDDL) with Large Language Model (LLM) opens new research topic in AI planning, particularly for complex real-world tasks. This paper introduces Image2PDDL, a novel framework that leverages Vision-Language Models (VLMs) to automatically convert images of initial states and descriptions of goal states into PDDL problems. By providin...
Traditional depression screening methods, such as the PHQ-9, are particularly challenging for children in pediatric primary care due to practical limitations. AI has the potential to help, but the scarcity of annotated datasets in mental health, combined with the computational costs of training, highlights the need for efficient, zero-shot approaches. In this work, we investigate the feasibility...
Recently, multimodal depression recognition for clinical interviews (MDRC) has recently attracted considerable attention. Existing MDRC studies main...
The Mental Health Question Answer (MHQA) task requires the seeker and supporter to complete the support process in one-turn dialogue. Given the rich...
Stigma has emerged as one of the major obstacles to effectively diagnosing depression, as it prevents users from open conversations about their stru...
The vast repositories of Electronic Health Records (EHR) and medical claims hold untapped potential for studying rare but critical events, such as s...
In today's interconnected society, social media platforms have become an important part of our lives, where individuals virtually express their thou...
Mental disorders have become a significant global public health issue, while the shortage of psychiatrists and inefficient training systems severely...
A mental health disorder is a clinically significant impairment in a persons intellect, emotional control, or behavior. Mental disorders and outpati...
Importance: Emergency department (ED) returns for mental health conditions pose a major healthcare burden, with 24-27% of patients returning within ...
Long-form mental health assessments pose unique challenges for large language models (LLMs), which often exhibit hallucinations or inconsistent reas...
Research has increasingly explored the application of artificial intelligence (AI) and machine learning (ML) within the mental health domain to enha...
The coronavirus pandemic corresponds to a serious global health crisis which not only changed the way people used to live but also how people behave...
Artificial Intelligence-Generated Content, a subset of Generative Artificial Intelligence, holds significant potential for advancing the e-health se...
Functional Magnetic Resonance Imaging (fMRI) provides useful insights into the brain function both during task or rest. Representing fMRI data using...
Machine learning bias in mental health is becoming an increasingly pertinent challenge. Despite promising efforts indicating that multitask approach...
Traditional in-person psychological counseling remains primarily niche, often chosen by individuals with psychological issues, while online automate...
Current speech encoding pipelines often rely on an additional text-based LM to get robust representations of human communication, even though SotA s...
Background: Adolescents are particularly vulnerable to mental disorders, with over 75% of cases manifesting before the age of 25. Research indicates...
Existing Theory of Mind (ToM) benchmarks diverge from real-world scenarios in three aspects: 1) they assess a limited range of mental states such as...