The Current Research Landscape on the Artificial Intelligence Application in the Management of Depressive Disorders

A Bibliometric Analysis

Bibliographic Data

ID15513973
AuthorsBach Xuan Tran (0000-0002-2191-3947, Institute for Preventive Medicine and Public Health, Hanoi Medical University, Hanoi 100000, Vietnam, corresponding author), Roger S McIntyre (0000-0003-4733-2523, University Health Network), Carl Latkin (0000-0002-7931-2116, Johns Hopkins University), Hai Thanh Phan (0000-0002-3424-0753, Duy Tan University), Giang Thu Vu (0000-0002-3470-4458, Trường ĐH Nguyễn Tất Thành), Huong Lan Thi Nguyen (0000-0001-9017-1978, Duy Tan University), Kenneth K Gwee (National University of Singapore), Cyrus S H Ho (0000-0002-7092-9566, National University Hospital), Roger C M Ho (0000-0001-9629-4493, National University of Singapore)
Year2019
Volume16
Issue12
Pages2150-2150
Publication date2019-06-18
Peer ReviewedYes
Open AccessYes
TypeARTICLE
VenueInternational Journal of Environmental Research and Public Health (JOURNAL)
Journal identifiersISSN: 1661-7827 • E-ISSN: 1660-4601
PublisherMultidisciplinary Digital Publishing Institute (PUBLISHER • CH)
DOI10.3390/ijerph16122150
PMID31216619
OpenAlexW2953301966
LanguageEN
Citations received6
References cited42

Artificial intelligence (AI)-based techniques have been widely applied in depression research and treatment. Nonetheless, there is currently no systematic review or bibliometric analysis in the medical literature about the applications of AI in depression. We performed a bibliometric analysis of the current research landscape, which objectively evaluates the productivity of global researchers or institutions in this field, along with exploratory factor analysis (EFA) and latent dirichlet allocation (LDA). From 2010 onwards, the total number of papers and citations on using AI to manage depressive disorder have risen considerably. In terms of global AI research network, researchers from the United States were the major contributors to this field. Exploratory factor analysis showed that the most well-studied application of AI was the utilization of machine learning to identify clinical characteristics in depression, which accounted for more than 60% of all publications. Latent dirichlet allocation identified specific research themes, which include diagnosis accuracy, structural imaging techniques, gene testing, drug development, pattern recognition, and electroencephalography (EEG)-based diagnosis. Although the rapid development and widespread use of AI provide various benefits for both health providers and patients, interventions to enhance privacy and confidentiality issues are still limited and require further research

Data science · Exploratory factor analysis · Field (mathematics · Latent Dirichlet allocation · Machine learning · Major depressive disorder · Psychiatry · Psychological intervention · Structural equation modeling · Topic model · Artificial Intelligence in Healthcare and Education · Clinical Psychology · Computer Science · Digital Mental Health Interventions · Functional Brain Connectivity Studies · Psychology · Artificial Intelligence

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