BACKGROUND: Text-based digital media platforms have revolutionized communication and information sharing, providing valuable access to knowledge and understanding in the fields of mental health and suicide prevention.
OBJECTIVE: This systematic review aimed to determine how machine learning and data analysis can be applied to text-based digital media data to understand mental health and aid suicide prevention.
METHODS: A systematic review of research papers from the following major electronic databases was conducted: Web of Science, MEDLINE, Embase (via MEDLINE), and PsycINFO (via MEDLINE). The database search was supplemented by a hand search using Google Scholar.
RESULTS: Overall, 19 studies were included, with five major themes as to how data analysis and machine learning techniques could be applied: (1) as predictors of personal mental health, (2) to understand how personal mental health and suicidal behavior are communicated, (3) to detect mental disorders and suicidal risk, (4) to identify help seeking for mental health difficulties, and (5) to determine the efficacy of interventions to support mental well-being.
CONCLUSIONS: Our findings show that data analysis and machine learning can be used to gain valuable insights, such as the following: web-based conversations relating to depression vary among different ethnic groups, teenagers engage in a web-based conversation about suicide more often than adults, and people seeking support in web-based mental health communities feel better after receiving online support. Digital tools and mental health apps are being used successfully to manage mental health, particularly through the COVID-19 epidemic, during which analysis has revealed that there was increased anxiety and depression, and web-based communities played a part in reducing isolation during the pandemic. Predictive analytics were also shown to have potential, and virtual reality shows promising results in the delivery of preventive or curative care. Future research efforts could center on optimizing algorithms to enhance the potential of text-based digital media analysis in mental health and suicide prevention. In addressing depression, a crucial step involves identifying the factors that contribute to happiness and using machine learning to forecast these sources of happiness. This could extend to understanding how various activities result in improved happiness across different socioeconomic groups. Using insights gathered from such data analysis and machine learning, there is an opportunity to craft digital interventions, such as chatbots, designed to provide support and address mental health challenges and suicide prevention.

UNASSIGNED: Our aim was to analyze the association between criminal behavior and impulsivity in individuals with drug addiction and investigate whether impulsiveness mediates the relationship between drug use severity and legal problems.
UNASSIGNED: This cross-sectional study included 773 men diagnosed with addiction (295 alcohol users and 478 users of crack/polysubstance) while undergoing addiction treatment. The BIS-11 and ASI-6 were applied to assess impulsivity, criminal behavior, and drug use.
UNASSIGNED: The prevalence of criminal behavior was 41.7% (n = 123) in alcohol users and 64.9% (n = 310) in users of crack/polysubstance. Earlier use of different substances and higher impulsivity scores were observed in individuals with criminal history. Mediation analyses revealed that impulsiveness acts as a mediator factor between substance use and criminal behavior, enhancing the severity of legal problems.
UNASSIGNED: Our findings can help in deciding on tailored treatment strategies, focusing not only on substance use, but also on the prevention of social problems, criminality, and impulsivity.

UNASSIGNED: The US has the highest maternal mortality rate among developed countries. The Centers for Disease Control and Prevention deems nearly all of these deaths preventable, especially those attributable to mental health conditions. Coordination between US health care and social service systems could help further characterize circumstances and risks associated with perinatal suicide mortality.
UNASSIGNED: To examine contextual and individual precipitating circumstances and risks associated with perinatal suicide.
UNASSIGNED: This cross-sectional observational study used a convergent mixed methods design to explore factors contributing to maternal suicides and deaths of undetermined intent (hereinafter, undetermined deaths) identified in National Violent Death Reporting System (NVDRS) data for January 1, 2003, to December 31, 2021. Analyses included decedents who were aged 10 to 50 years and pregnant or post partum at death (collectively, the perinatal group) and demographically matched female decedents who were not pregnant or recently pregnant (nonperinatal group) at death. Analyses were performed between December 2022 and December 2023.
UNASSIGNED: Pregnancy status at death (perinatal or nonperinatal).
UNASSIGNED: The main outcomes included contributing circumstances associated with suicides and undetermined deaths cited in coroner, medical examiner, or law enforcement case narratives. The study examined quantitative differences between groups using a matched analysis and characterized key themes of salient suicide circumstances using qualitative content analysis.
UNASSIGNED: This study included 1150 perinatal decedents identified in the NVDRS: 456 (39.6%) were pregnant at death, 203 (17.7%) were pregnant within 42 days of death, and 491 (42.7%) were pregnant within 43 to 365 days before death, yielding 694 postpartum decedents. The nonperinatal comparison group included 17 655 female decedents aged 10 to 50 years. The mean (SD) age was 29.1 (7.4) years for perinatal decedents and 35.8 (10.8) years for nonperinatal decedents. Compared with matched nonperinatal decedents, perinatal decedents had higher odds of the following identified contributing circumstances: intimate partner problems (IPPs) (odds ratio [OR], 1.45 [95% CI, 1.23-1.72]), recent argument (OR, 1.33 [95% CI, 1.09-1.61]), depressed mood (OR, 1.39 [95% CI, 1.19-1.63]), substance abuse or other abuse (OR, 1.21 [95% CI, 1.03-1.42]), physical health problems (OR, 1.37 [95% CI, 1.09-1.72]), and death of a family member or friend (OR, 1.47 [95% CI, 1.06-2.02]). The findings of the qualitative analysis emphasized the importance of mental health and identified 128 decedents (12.4%) with postpartum depression.
UNASSIGNED: This study provides insights into complex factors surrounding maternal suicide, and it highlights opportunities for further research to understand long-term consequences of perinatal mental health. These findings also underscore the need for targeted evidence-based interventions and effective policies targeting mental health, substance use, and IPPs to prevent maternal suicide and enhance maternal health outcomes.