Personal Mental Health Navigator: Harnessing the Power of Data, Personal
Models, and Health Cybernetics to Promote Psychological Well-being
- URL: http://arxiv.org/abs/2012.09131v1
- Date: Tue, 15 Dec 2020 18:34:09 GMT
- Title: Personal Mental Health Navigator: Harnessing the Power of Data, Personal
Models, and Health Cybernetics to Promote Psychological Well-being
- Authors: Amir M. Rahmani, Jocelyn Lai, Salar Jafarlou, Asal Yunusova, Alex. P.
Rivera, Sina Labbaf, Sirui Hu, Arman Anzanpour, Nikil Dutt, Ramesh Jain,
Jessica L. Borelli
- Abstract summary: We present the notion of Personalized Mental Health Navigation (MHN)
MHN deploys a continuous cyclic loop of measurement, estimation, guidance, to steer the individual's mental health state towards a healthy zone.
We demonstrate the feasibility of the personalized MHN approach via a 12-month pilot case study for holistic stress management in college students.
- Score: 2.491393414479041
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Traditionally, the regime of mental healthcare has followed an episodic
psychotherapy model wherein patients seek care from a provider through a
prescribed treatment plan developed over multiple provider visits. Recent
advances in wearable and mobile technology have generated increased interest in
digital mental healthcare that enables individuals to address episodic mental
health symptoms. However, these efforts are typically reactive and
symptom-focused and do not provide comprehensive, wrap-around, customized
treatments that capture an individual's holistic mental health model as it
unfolds over time. Recognizing that each individual is unique, we present the
notion of Personalized Mental Health Navigation (MHN): a therapist-in-the-loop,
cybernetic goal-based system that deploys a continuous cyclic loop of
measurement, estimation, guidance, to steer the individual's mental health
state towards a healthy zone. We outline the major components of MHN that is
premised on the development of an individual's personal mental health state,
holistically represented by a high-dimensional cover of multiple knowledge
layers such as emotion, biological patterns, sociology, behavior, and
cognition. We demonstrate the feasibility of the personalized MHN approach via
a 12-month pilot case study for holistic stress management in college students
and highlight an instance of a therapist-in-the-loop intervention using MHN for
monitoring, estimating, and proactively addressing moderately severe depression
over a sustained period of time. We believe MHN paves the way to transform
mental healthcare from the current passive, episodic, reactive process (where
individuals seek help to address symptoms that have already manifested) to a
continuous and navigational paradigm that leverages a personalized model of the
individual, promising to deliver timely interventions to individuals in a
holistic manner.
Related papers
- 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) - MentalAgora: A Gateway to Advanced Personalized Care in Mental Health through Multi-Agent Debating and Attribute Control [40.21489535255022]
MentalAgora is a novel framework employing large language models enhanced by interaction between multiple agents for tailored mental health support.
This framework operates through three stages: strategic debating, tailored counselor creation, and response generation.
Our evaluations, including experiments and user studies, demonstrate that MentalAgora aligns with professional standards and effectively meets user preferences.
arXiv Detail & Related papers (2024-07-03T01:19:38Z) - Individual brain parcellation: Review of methods, validations and applications [7.159138402684875]
Accurate mapping of brain functional regions at the individual level is pivotal for a comprehensive understanding of the variations in brain function and behaviors.
With the development of neuroimaging and machine learning techniques, studies on individual brain parcellation are booming.
arXiv Detail & Related papers (2024-07-01T05:48:05Z) - Predicting Depression and Anxiety: A Multi-Layer Perceptron for
Analyzing the Mental Health Impact of COVID-19 [1.9809980686152868]
We introduce a multi-layer perceptron (MLP) called the COVID-19 Depression and Anxiety Predictor (CoDAP) to predict mental health trends during the COVID-19 pandemic.
Our method utilizes a comprehensive dataset, which tracked mental health symptoms weekly over ten weeks during the initial COVID-19 wave (April to June 2020) in a diverse cohort of U.S. adults.
This model not only predicts patterns of anxiety and depression during the pandemic but also unveils key insights into the interplay of demographic factors, behavioral changes, and social determinants of mental health.
arXiv Detail & Related papers (2024-03-09T22:49:04Z) - 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) - MentalHealthAI: Utilizing Personal Health Device Data to Optimize
Psychiatry Treatment [1.696974372855528]
Mental health disorders remain a significant challenge in modern healthcare, with diagnosis and treatment often relying on subjective patient descriptions and past medical history.
We propose a personalized mental health tracking and mood prediction system that utilizes patient physiological data collected through personal health devices.
arXiv Detail & Related papers (2023-07-09T22:30:47Z) - 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) - fMRI Neurofeedback Learning Patterns are Predictive of Personal and
Clinical Traits [62.997667081978825]
We obtain a personal signature of a person's learning progress in a self-neuromodulation task, guided by functional MRI (fMRI)
The signature is based on predicting the activity of the Amygdala in a second neurofeedback session, given a similar fMRI-derived brain state in the first session.
arXiv Detail & Related papers (2021-12-21T06:52:48Z) - Learning Language and Multimodal Privacy-Preserving Markers of Mood from
Mobile Data [74.60507696087966]
Mental health conditions remain underdiagnosed even in countries with common access to advanced medical care.
One promising data source to help monitor human behavior is daily smartphone usage.
We study behavioral markers of daily mood using a recent dataset of mobile behaviors from adolescent populations at high risk of suicidal behaviors.
arXiv Detail & Related papers (2021-06-24T17:46:03Z) - 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)
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.