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

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.

Association between Perceived Stressfulness of Stressful Life Events and the Suicidal Risk in Chinese Patients with Major Depressive Disorder

Background： Patients with major depressive disorder （MDD） usually have high risk of suicidality. Few studies have investigated the effects of stressful life events （SLEs） on the risk of suicide in Chinese patients who have developed MDD. This study aimed to investigate the impact of SLEs on suicidal risk in Chinese patients with MDD. Methods： In total, 1029 patients with MDD were included from nine psychiatric hospitals to evaluate the impact of SLEs on suicidal risk. Patients fulfilling the Mini-International Neuropsychiatric Interview （MINI） criteria for MDD were included in the study. Patients were excluded if they had lifetime or current diagnoses of psychotic disorder, bipolar disorder, and alcohol or substance dependence. Depressive symptoms were assessed by the 17-item Harnilton Depression Scale （HAMD-17）. The suicidal risk of MDD patients was determined by the suicide risk module of MINI. SLEs were assessed by the Life Events Scale. Results： No gender difference was found for suicidal risk in MDD patients. Patients with suicidal risk had younger ages, lower education levels, more drinking behavior, and lower marriage rate, and fewer people had child and more severe depressive symptoms than nonsuicidal risk group. High-level perceived stressfulness （HPS） and number of SLEs that patients were exposed to were significantly greater in patients with suicidal risk than patients without. In multivariate logistic analysis, HPS of SLEs （odds ratio [OR] = 1.54, 95% confidence interval [C1]： 1.16-2.05, P = 0.003） and depressive symptoms （OR = 1.08.95% CI： 1.05-1.11, P 〈 0.001 ） were associated with suicidal risk even after adjustment of gender, age, marriage, drinking behavior, and childless. Conclusions： HPS of SLEs is associated with suicide risk in Chinese patients with MDD. Further suicide prevention programs targeting this risk factor are needed. Trial Registration： ClinicalTrials.gov： NCT02023567; https：//clinicaltrials.gov/ct2/show/NCTO2023567？term=NCTO2023567＆rank=l.