Literature DB >> 32604065

AI in mental health.

Simon D'Alfonso1.   

Abstract

With the advent of digital approaches to mental health, modern artificial intelligence (AI), and machine learning in particular, is being used in the development of prediction, detection and treatment solutions for mental health care. In terms of treatment, AI is being incorporated into digital interventions, particularly web and smartphone apps, to enhance user experience and optimise personalised mental health care. In terms of prediction and detection, modern streams of abundant data mean that data-driven AI methods can be employed to develop prediction/detection models for mental health conditions. In particular, an individual's 'digital exhaust', the data gathered from their numerous personal digital device and social media interactions, can be mined for behavioural or mental health insights. Language, long considered a window into the human mind, can now be quantitatively harnessed as data with powerful computer-based natural language processing to also provide a method of inferring mental health. Furthermore, natural language processing can also be used to develop conversational agents used for therapeutic intervention.
Copyright © 2020 Elsevier Ltd. All rights reserved.

Entities:  

Year:  2020        PMID: 32604065     DOI: 10.1016/j.copsyc.2020.04.005

Source DB:  PubMed          Journal:  Curr Opin Psychol        ISSN: 2352-250X


  12 in total

1.  Predicting Mental Health Problems with Automatic Identification of Metaphors.

Authors:  Nan Shi; Dongyu Zhang; Lulu Li; Shengjun Xu
Journal:  J Healthc Eng       Date:  2021-04-30       Impact factor: 2.682

2.  Digital Mental Health Challenges and the Horizon Ahead for Solutions.

Authors:  Luke Balcombe; Diego De Leo
Journal:  JMIR Ment Health       Date:  2021-03-29

3.  Challenges of Building, Deploying, and Using AI-Enabled Telepsychiatry Platforms for Clinical Practice Among Urban Indians: A Qualitative Study.

Authors:  Arunkumar Annamalai
Journal:  Indian J Psychol Med       Date:  2020-12-19

4.  Tracing the Growth, Gaps, and Characteristics in Positive Education Science: A Long-Term, Large-Scale Review of the Field.

Authors:  Lea Waters; Daniel Loton
Journal:  Front Psychol       Date:  2021-12-03

5.  The Role of Artificial Intelligence and Machine Learning Amid the COVID-19 Pandemic: What Lessons Are We Learning on 4IR and the Sustainable Development Goals.

Authors:  David Mhlanga
Journal:  Int J Environ Res Public Health       Date:  2022-02-08       Impact factor: 3.390

Review 6.  Implementation of Cognitive Behavioral Therapy in e-Mental Health Apps: Literature Review.

Authors:  Kerstin Denecke; Nicole Schmid; Stephan Nüssli
Journal:  J Med Internet Res       Date:  2022-03-10       Impact factor: 7.076

7.  Diagnosis of Depressive Disorder Model on Facial Expression Based on Fast R-CNN.

Authors:  Young-Shin Lee; Won-Hyung Park
Journal:  Diagnostics (Basel)       Date:  2022-01-27

8.  Why Do Public Safety Personnel Seek Tailored Internet-Delivered Cognitive Behavioural Therapy? An Observational Study of Treatment-Seekers.

Authors:  Hugh C McCall; Caeleigh A Landry; Adeyemi Ogunade; R Nicholas Carleton; Heather D Hadjistavropoulos
Journal:  Int J Environ Res Public Health       Date:  2021-11-15       Impact factor: 3.390

9.  Digital technological interventions in mental health care.

Authors:  Kalpana Srivastava; Suprakash Chaudhury; Sana Dhamija; Jyoti Prakash; Kaushik Chatterjee
Journal:  Ind Psychiatry J       Date:  2021-03-15

Review 10.  Ethics and Law in Research on Algorithmic and Data-Driven Technology in Mental Health Care: Scoping Review.

Authors:  Piers Gooding; Timothy Kariotis
Journal:  JMIR Ment Health       Date:  2021-06-10
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