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The Utility of Artificial Intelligence for Mood Analysis, Depression Detection, and Suicide Risk Management - 2020

The Utility Of Artificial Intelligence For Mood Analysis, Depression Detection, And Suicide Risk Management

Research Area:  Machine Learning


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.


Author(s) Name:  Bahman Zohuri and Siamak Zadeh

Journal name:  Journal of Health Science

Conferrence name:  

Publisher name:  DAVID PUBLISHING

DOI:  10.17265/2328-7136/2020.02.003

Volume Information:  Volume 8, Pages: 67-73