Identify adolescents’ help-seeking intention on suicide through self- and caregiver’s assessments of psychobehavioral problems: Deep clustering of the Tokyo TEEN Cohort study

Background Psychopathological and behavioral problems in adolescence are highly comorbid, making their developmental trajectories complex and unclear partly due to technical limitations. We aimed to classify these trajectories using deep learning and identify predictors of cluster membership. Methods We conducted a population-based cohort study on 3171 adolescents from three Tokyo municipalities, with 2344 pairs of […]

Practical use of ChatGPT in psychiatry for treatment plan and psychoeducation

Artificial Intelligence (AI) has revolutionized various fields, including medicine and mental health support. One promising application is ChatGPT, an advanced conversational AI model that uses deep learning techniques to  provide human-like responses. This review paper explores the potential impact of Chat-GPT in psychiatry and its various applications, highlighting its role in therapy and counseling techniques, […]

Deep sequential neural network models improve stratification of suicide attempt risk among US veterans

Objective: To apply deep neural networks (DNNs) to longitudinal EHR data in order to predict suicide attempt risk among veterans. Local explainability techniques were used to provide explanations for each prediction with the goal of ultimately improving outreach and intervention efforts. Materials and methods: The DNNs fused demographic information with diagnostic, prescription, and procedure codes. Models were […]

ChatGPT, artificial intelligence, and suicide prevention: A call for a targeted and concerted research effort

There is an ever-increasing speed in digital transformation, including health communication and healthcare. ChatGPT is one of the most recent milestones in this regard, having been introduced to the public by OpenAI in  November 2022. Although ChatGPT is still under development, it is likely that we will face a widespread rollout of such tools during […]

Suicidal ideation detection: A review of machine learning and applications

Suicide is a critical issue in modern society. Early detection and prevention of suicide attempts should be addressed to save people’s life. Current suicidal ideation detection (SID) methods include clinical methods based on the interaction between social workers or experts and the targeted individuals and machine learning techniques with feature engineering or deep learning for […]

Unveiling the role of social media in mental health: A GAN-based deep learning framework for suicide prevention

In recent years, there has been a significant increase in user participation on social networking media sites. These platforms generate vast amounts of diverse data that have a substantial impact on the mental health of the general public. Suicide, being a leading cause of death globally, has drawn the attention of researchers. The World Health […]

A review of machine & deep learning techniques in detecting suicidal tendency

In recent years, the number of deaths due to suicide has increased. Suicide is becoming one of the major causes of death across the whole world. Early detection and prevention of suicide attempts should be addressed to save people’s life. Thus, several studies  found that people who are contemplating suicide can be identified by analyzing […]

Public surveillance of social media for suicide using advanced deep learning models in Japan: Time series study from 2012 to 2022

Background: Social media platforms have been increasingly used to express suicidal thoughts, feelings, and acts, raising public concerns over time. A large body of literature has explored the suicide risks identified by people’s expressions on social media. However, there is not enough evidence to conclude that social media provides public surveillance for suicide without aligning […]

Deep learning techniques for suicide and depression detection from online social media: A scoping review

Psychological health, i.e., citizens’ emotional and mental well-being, is one of the most neglected public health issues. Depression is the most common mental health issue and the leading cause of suicide and self-injurious behavior. Clinical diagnosis of these mental health issues is expensive and also ignored due to social stigma and lack of awareness. Nowadays, online social […]

A picture may be worth a thousand lives: An interpretable artificial intelligence strategy for predictions of suicide risk from social media images

The promising research on Artificial Intelligence usages in suicide prevention has principal gaps, including black box methodologies, inadequate outcome measures, and scarce research on non-verbal inputs, such as social media images (despite their popularity today, in our digital era). This study addresses these gaps and combines theory-driven and bottom-up strategies to construct a hybrid and […]

Artificial intelligence assisted tools for the detection of anxiety and depression leading to suicidal ideation in adolescents: A review

Epidemiological studies report high levels of anxiety and depression amongst adolescents. These psychiatric conditions and complex interplays of biological, social and environmental factors are important risk factors for suicidal behaviours and suicide, which show a peak in late adolescence and early adulthood. Although deaths by suicide have fallen globally in recent years, suicide deaths are […]