MindGuard: Towards Accessible and Sitgma-free Mental Health First Aid via Edge LLM
- URL: http://arxiv.org/abs/2409.10064v1
- Date: Mon, 16 Sep 2024 07:58:56 GMT
- Title: MindGuard: Towards Accessible and Sitgma-free Mental Health First Aid via Edge LLM
- Authors: Sijie Ji, Xinzhe Zheng, Jiawei Sun, Renqi Chen, Wei Gao, Mani Srivastava,
- Abstract summary: Mental health disorders affect nearly one in four people worldwide.
Stigma discourages over half of those affected from seeking help.
This paper presents MindGuard, an accessible, stigma-free, and professional mobile mental healthcare system.
- Score: 5.961343130822046
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Mental health disorders are among the most prevalent diseases worldwide, affecting nearly one in four people. Despite their widespread impact, the intervention rate remains below 25%, largely due to the significant cooperation required from patients for both diagnosis and intervention. The core issue behind this low treatment rate is stigma, which discourages over half of those affected from seeking help. This paper presents MindGuard, an accessible, stigma-free, and professional mobile mental healthcare system designed to provide mental health first aid. The heart of MindGuard is an innovative edge LLM, equipped with professional mental health knowledge, that seamlessly integrates objective mobile sensor data with subjective Ecological Momentary Assessment records to deliver personalized screening and intervention conversations. We conduct a broad evaluation of MindGuard using open datasets spanning four years and real-world deployment across various mobile devices involving 20 subjects for two weeks. Remarkably, MindGuard achieves results comparable to GPT-4 and outperforms its counterpart with more than 10 times the model size. We believe that MindGuard paves the way for mobile LLM applications, potentially revolutionizing mental healthcare practices by substituting self-reporting and intervention conversations with passive, integrated monitoring within daily life, thus ensuring accessible and stigma-free mental health support.
Related papers
- MAGI: Multi-Agent Guided Interview for Psychiatric Assessment [50.6150986786028]
We present MAGI, the first framework that transforms the gold-standard Mini International Neuropsychiatric Interview (MINI) into automatic computational navigation.
We show that MAGI advances LLM- assisted mental health assessment by combining clinical rigor, conversational adaptability, and explainable reasoning.
arXiv Detail & Related papers (2025-04-25T11:08:27Z) - MentalChat16K: A Benchmark Dataset for Conversational Mental Health Assistance [13.373260490163709]
MentalChat16K is an English benchmark dataset combining a synthetic mental health counseling dataset and a dataset of anonymized transcripts from interventions between Behavioral Health Coaches and Caregivers of patients in palliative or hospice care.
Covering a diverse range of conditions like depression, anxiety, and grief, this curated dataset is designed to facilitate the development and evaluation of large language models for conversational mental health assistance.
arXiv Detail & Related papers (2025-03-13T20:25:10Z) - MentalArena: Self-play Training of Language Models for Diagnosis and Treatment of Mental Health Disorders [59.515827458631975]
Mental health disorders are one of the most serious diseases in the world.
Privacy concerns limit the accessibility of personalized treatment data.
MentalArena is a self-play framework to train language models.
arXiv Detail & Related papers (2024-10-09T13:06:40Z) - MindScape Study: Integrating LLM and Behavioral Sensing for Personalized AI-Driven Journaling Experiences [6.120545056775202]
MindScape pioneers a novel approach to AI-powered journaling by integrating passively collected behavioral patterns.
This integration creates a highly personalized and context-aware journaling experience.
We present an 8-week exploratory study with 20 college students, demonstrating the MindScape app's efficacy in enhancing positive affect.
arXiv Detail & Related papers (2024-09-15T01:10:46Z) - PsychoGAT: A Novel Psychological Measurement Paradigm through Interactive Fiction Games with LLM Agents [68.50571379012621]
Psychological measurement is essential for mental health, self-understanding, and personal development.
PsychoGAT (Psychological Game AgenTs) achieves statistically significant excellence in psychometric metrics such as reliability, convergent validity, and discriminant validity.
arXiv Detail & Related papers (2024-02-19T18:00:30Z) - Differential Private Federated Transfer Learning for Mental Health Monitoring in Everyday Settings: A Case Study on Stress Detection [4.439102809224707]
Mental health conditions necessitate efficient monitoring to mitigate their adverse impacts on life quality.
Existing approaches struggle with vulnerabilities to certain cyber-attacks and data insufficiency in real-world applications.
We introduce a differential private federated transfer learning framework for mental health monitoring to enhance data privacy and enrich data sufficiency.
arXiv Detail & Related papers (2024-02-16T18:00:04Z) - MindfulDiary: Harnessing Large Language Model to Support Psychiatric
Patients' Journaling [16.929899228710852]
We present MindfulDiary, a mobile journaling app incorporating an Large Language Model (LLMs) to help psychiatric patients document daily experiences through conversation.
We found that MindfulDiary supported patients in consistently enriching their daily records and helped psychiatrists better empathize with their patients through an understanding of their thoughts and daily contexts.
arXiv Detail & Related papers (2023-10-08T17:00:04Z) - Handwriting and Drawing for Depression Detection: A Preliminary Study [53.11777541341063]
Short-term covid effects on mental health were a significant increase in anxiety and depressive symptoms.
The aim of this study is to use a new tool, the online handwriting and drawing analysis, to discriminate between healthy individuals and depressed patients.
arXiv Detail & Related papers (2023-02-05T22:33:49Z) - Mental Illness Classification on Social Media Texts using Deep Learning
and Transfer Learning [55.653944436488786]
According to the World health organization (WHO), approximately 450 million people are affected.
Mental illnesses, such as depression, anxiety, bipolar disorder, ADHD, and PTSD.
This study analyzes unstructured user data on Reddit platform and classifies five common mental illnesses: depression, anxiety, bipolar disorder, ADHD, and PTSD.
arXiv Detail & Related papers (2022-07-03T11:33:52Z) - MET: Multimodal Perception of Engagement for Telehealth [52.54282887530756]
We present MET, a learning-based algorithm for perceiving a human's level of engagement from videos.
We release a new dataset, MEDICA, for mental health patient engagement detection.
arXiv Detail & Related papers (2020-11-17T15:18:38Z) - Passive detection of behavioral shifts for suicide attempt prevention [0.0]
We present a non-invasive machine learning model to detect behavioral shifts in psychiatric patients from unobtrusive data collected by a smartphone app.
Our clinically validated results shed light on the idea of an early detection mobile tool for the task of suicide attempt prevention.
arXiv Detail & Related papers (2020-11-14T11:44:43Z) - Anxiety Detection Leveraging Mobile Passive Sensing [53.11661460916551]
Anxiety disorders are the most common class of psychiatric problems affecting both children and adults.
Leveraging passive and unobtrusive data collection from smartphones could be a viable alternative to classical methods.
eWellness is an experimental mobile application designed to track a full-suite of sensor and user-log data off an individual's device in a continuous and passive manner.
arXiv Detail & Related papers (2020-08-09T20:22:52Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.