Prediction of suicidal ideation in children and adolescents using machine learning and deep learning algorithm: A case study in South Korea where suicide is the leading cause of death

Soomin, Shin; Kim, Kyungwon

doi:10.1016/j.ajp.2023.103725

Cited by 8 publications

(13 citation statements)

References 39 publications

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“… Findings indicate increased emotional distress linked to government-imposed restrictions, school closures, and economic challenges. Shin 14 Prediction of suicidal ideation in children and adolescents using machine learning and deep learning algorithm: A case study in South Korea where suicide is the leading cause of death Asian Journal of Psychiatry Population (P): Children and adolescents in South Korea. Intervention (I): Machine learning and deep learning algorithms to predict suicidal ideation.…”

Section: Resultsmentioning

confidence: 99%

“…There is a profound trend toward integrating advanced analytical techniques to inform and enhance public policy and health strategies. 14–16 , 18–20 Advanced analytical techniques, such as machine learning and deep learning algorithms, were used to predict suicidal ideation among children and adolescents in South Korea with high accuracy, thereby providing critical data-driven insights to inform and enhance public policy on suicide prevention. 14 A predictive modeling method was used to delve into individual behaviors surrounding voluntary isolation, identifying the psychological and sociocultural factors influencing compliance.…”

Section: Discussionmentioning

confidence: 99%

“… 14–16 , 18–20 Advanced analytical techniques, such as machine learning and deep learning algorithms, were used to predict suicidal ideation among children and adolescents in South Korea with high accuracy, thereby providing critical data-driven insights to inform and enhance public policy on suicide prevention. 14 A predictive modeling method was used to delve into individual behaviors surrounding voluntary isolation, identifying the psychological and sociocultural factors influencing compliance. 15 Additionally, advanced analytical techniques were used to construct scenarios of aging in smart environments in the United States in 2050, illuminating the uncertainties, challenges, and opportunities presented by the convergence of an aging population and increasingly sensed environments to inform and enhance public policy on aging and healthcare.…”

Section: Discussionmentioning

confidence: 99%

“… 18 Such research collectively underscores the intricate relationship between technology and human conduct during public health crises. While another study investigates policy analysis, 14 previous research examines the connection between individual mental states and resulting behaviors. 15 This underscores a growing recognition of the necessity for policy measures that consider human psychology, a theme increasingly pertinent amid global health challenges.…”

Section: Discussionmentioning

confidence: 99%

“…Additionally, a connection between individual mental states such as feelings of being caged, responsibility, and fear to behavioral outcomes was also identified by the research team. 14 , 15 This reflects a growing awareness of the need for policy interventions that are sensitive to human psychology, a theme that is becoming increasingly relevant in the face of global health challenges. Therefore, it is vital to address mental health concerns and provide the necessary support by understanding and respecting cultural practices while promoting measures that align with these socio-cultural drivers may improve compliance with COVID-19 measures.…”

Section: Discussionmentioning

confidence: 99%

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A Systematic Review of Artificial Intelligence Used to Predict Loneliness, Social Isolation, and Drug Use During the COVID-19 Pandemic

Torres,

Wenke,

Lieneck

et al. 2024

JMDH

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This systematic literature review evaluates the role of machine learning, artificial intelligence (AI), and social determinants of health (SDOH) in identifying loneliness during the COVID-19 pandemic. By examining various studies and articles through a comprehensive search of databases EBSCOhost, Medline Complete, Academic Search Complete, Directory of Open Access Journals, and Complementary Index, the research team sought to discern consistent themes and patterns. We identified four constructs central to understanding the impact of the pandemic on societal well-being: (1) the prediction of compliance with COVID-19 measures, (2) the prediction of loneliness and its effects, (3) the prediction of well-being and social inclusion, and (4) the prediction of drug use. Within these constructs, prevalent themes related to opioid overdose, stress levels, mental health, well-being, and cognitive decline emerged. The adherence to the PRISMA 2020 checklist has resulted in a PRISMA flow diagram that categorizes the selected literature. The findings of this review, including the proportion of studies predicting various attributes related to loneliness, demonstrate the critical intersections between machine learning, AI, SDOH, and the psychosocial phenomenon of loneliness amidst a global health crisis. The review results provide a summary of the occurrences and predictive percentages of each construct as determined by the literature, contributing to a nuanced understanding of the pandemic’s multifaceted impact on loneliness, social isolation, and drug use. Using AI to predict these constructs has remarkable capabilities in identifying individuals at risk and facilitating timely interventions to mitigate adverse outcomes and promote mental health resilience in the face of challenges such as the COVID-19 pandemic. Moving forward, future research is warranted to refine AI algorithms, validate predictive models and utilize AI-based interventions in healthcare and mental health services while ensuring data security, and individuals’ privacy.

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