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Article
Author(s)
Bahman Zohuri1,2 and Siamak Zadeh1
Full-Text PDF XML 23425 Views
DOI:10.17265/2328-7136/2020.02.003
Affiliation(s)
1. Ageno School of Business, Golden Gate University, San Francisco, California 94105, USA
2. Galaxy Advanced Engineering, Albuquerque, New Mexico 87111, USA
ABSTRACT
Mood disorders are often an indication or a sign of depression, and
individuals suffering from mood swings may face higher probability and
increased suicidal tendencies. Depression—also called “clinical depression” or
a “depressive disorder”—is a mood disorder that adversely impacts how an individual feels,
thinks, and handles daily activities, such as sleeping, eating, or working. To
be diagnosed with depression, symptoms must be present most of the time, nearly
every day for at least minimum of 2 to 3 weeks. Feeling sad or having low
emotional energy may be common among people. For most, however, these feelings
are transitory and can be managed by changing daily life routines. But for
some, prolonged mood disorders can lead to depression and foster suicidal
tendencies. Suicide is a major public health concern. Over 47,000 people died
by suicide in the United States in 2017. It is the 10th leading cause of death
overall according to NIMH (National Institute of Mental Health). Suicide is
complicated and tragic, but it is often preventable. Identifying the warning
signs for suicide and how to get help can be a major mitigating factor. In this
short communication, we are reviewing the promise and limitations of AI
(artificial intelligence) with its integrated tools such as ML (machine
learning) and DL (deep learning) for mood analysis as a means for detecting
early signs of depression and increased suicidal tendencies for possible
suicide risk management.
KEYWORDS
Depression, suicide attempt and suicide rate, youngsters, suicide risk management, augmentation of AI in depression treatment and suicide management.
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