Resource Tag: MACHINE LEARNING

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 […]

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 […]

Despite improvements in the detection and treatment of severe mental disorders, suicide remains a significant public health concern. Suicide prevention and control initiatives can benefit greatly from a thorough comprehension and foreseeability of suicide patterns. Understanding suicide patterns, especially through social media data analysis, can help in suicide prevention and control efforts. The objective of […]

Background: Suicide has been considered an important public health issue for years and is one of the main causes of death worldwide. Despite prevention strategies being applied, the rate of suicide has not changed substantially over the past decades. Suicide risk has proven extremely difficult to assess for medical specialists, and traditional methodologies deployed have been […]

Suicide is one of the most serious social health issues that exists in today’s culture. Suicidal ideation, also known as suicidal thoughts, refers to people’s plans to commit suicide. It can be used as a suicide risk  measure. India is among the top countries among in the world to have annual suicide rate. Social networks […]

Purpose: Machine learning (ML) has shown promise in modelling future self-harm but is yet to be applied to key questions facing clinical services. In a cohort of young people accessing primary mental health care, this study aimed to establish (1) the performance of models predicting deliberate self-harm (DSH) compared to suicide attempt (SA), (2) the performance […]

The rise in technological advancements and Social Networking Sites (SNS) made people more engaged in their virtual lives. Research has revealed that people feel more comfortable posting their feelings, including suicidal thoughts, on SNS than discussing them through face-to-face settings due to the social stigma associated with mental health. This research study aims to develop a multi-class machine […]

Purpose Machine learning (ML) has shown promise in modelling future self-harm but is yet to be applied to key questions facing clinical services. In a cohort of young people accessing primary mental health care, this study aimed to establish (1) the performance of models predicting deliberate self-harm (DSH) compared to suicide attempt (SA), (2) the […]

Background Targeted interventions for suicide prevention rely on adequate identification of groups at elevated risk. Several risk factors for suicide are known, but little is known about the interactions between risk factors. Interactions between risk factors may aid in detecting more specific sub-populations at higher risk. Methods Here, we use a novel machine learning heuristic […]

Suicide risk prediction models can identify individuals for targeted intervention. Discussions of transparency, explainability, and transportability in machine learning presume complex prediction models with many variables outperform simpler models. We compared random forest, artificial neural network, and ensemble models with 1500 temporally defined predictors to logistic regression models. Data from 25,800,888 mental health visits made […]

Purpose Machine learning (ML) has shown promise in modelling future self-harm but is yet to be applied to key questions facing clinical services. In a cohort of young people accessing primary mental health care, this study aimed to establish (1) the performance of models predicting deliberate self-harm (DSH) compared to suicide attempt (SA), (2) the […]

This project describes content analysis of text with to identify suicidal tendencies and types. This article also describes how to make a sentence classifier that uses a neural network created using various libraries created for machine learning in the Python programming language. Attention is paid to the problem of teenage suicide and «groups of death» […]

In this study, we implemented machine learning models that can detect suicidality posts on Twitter. We randomly selected and annotated 20,000 tweets and explored metadata and text features to build effective models. Metadata features were studied in great details to understand their possibility and importance in suicidality detection models. Results showed that posting type (i.e., […]

Background: Suicidal behaviors, including suicide deaths and attempts, are major public health concerns. However, previous suicide models required a huge amount of input features, resulting in limited applicability in clinical practice. Objective: We aimed to construct applicable models (ie, with limited features) for short- and long-term suicidal behavior prediction. We further validated these models among […]

Due to the high rate of suicide all over the world resulting in about 800,000 people dying by suicide each year. The instances where suicide victims constantly publish suicide messages deliberately to express their feelings on social media, there is need to address suicide issues, and how suicide can be prevented. Therefore, as a solution […]

The coronavirus disease 2019 (COVID-19) pandemic has created a global health crisis, with disproportionate effects on vulnerable sociodemographic groups. Although the pandemic is showing potential to increase suicide ideation (SI), we know little about which sociodemographic characteristics or COVID-19 experiences are associated with SI. Our United States-based sample (n = 837 adults [mean age = […]

Background: Suicide is a leading cause of death, and rates of attempted suicide have increased during the COVID-19 pandemic. The under-diagnosed psychiatric phenotype of dissociation is associated with elevated suicidal self-injury; however, it has largely been left out of attempts to predict and prevent suicide. Objective: We designed an artificial intelligence approach to identify dissociative patients and […]

Suicide, as an increasingly prominent social problem, has attracted widespread social attention in the mental health field. Traditional suicide clinical assessment and risk questionnaires lack timeliness and proactivity, and high-risk groups often conceal their intentions, which is not conducive to early suicide prevention. In this study, we used machine-learning algorithms to extract text features from […]

Background: Elevated psychological distress has demonstrated impacts on individuals’ health. Reliable and efficient ways to detect distress are key to early intervention. Artificial intelligence has the potential to detect states of emotional distress in an accurate, efficient, and timely manner. Objective: The aim of this study was to automatically classify short segments of speech obtained […]

Using Natural Language Processing (NLP), we are able to analyze text from suicidal individuals. This can be done using a variety of methods. I analyzed a dataset of a girl named Victoria who died by suicide. I used a machine learning method to train using a different dataset and tested it on her diary entries  […]

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 […]

Objective Suicide prevention is a major priority in Native American communities. We used machine learning with community-based suicide surveillance data to better identify those most at risk. Method This study leverages data from the Celebrating Life program operated by the White Mountain Apache Tribe in Arizona and in partnership with Johns Hopkins University. We examined […]

Objective The Centers for Disease Control and Prevention (CDC) monitor accidental and intentional deaths to answer questions that are critical for the development of effective prevention and resource allocation. CDC’s National Violent Death Reporting System (NVDRS) is a major innovation in surveillance linking individual-level data from multiple sources. However, suicide underreporting is common, particularly from […]