Resource Tag: NATURAL LANGUAGE PROCESSING (COMPUTER SCIENCE)

ChatGPT, an artificial intelligence language model developed by OpenAI, holds the potential for contributing to the field of mental health. Nevertheless, although ChatGPT theoretically shows promise, its clinical abilities in suicide prevention, a significant mental health concern, have yet to be demonstrated. To address this knowledge gap, this study aims to compare ChatGPT’s assessments of […]

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 suicide […]

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 […]

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 […]

This paper investigates a crucial aspect of mental health by exploring the detection of suicidal ideation in spoken phone conversations between callers and counselors at a suicide prevention hotline.These conversations can be lengthy, noisy, and cover a broad range of topics, making it challenging for NLP models to accurately identify the caller’s suicidal ideation. To […]

With the increased awareness of situations of mental crisis and their societal impact, online services providing emergency support are becoming commonplace in many countries. Computational models, trained on discussions between help-seekers and providers, can support suicide prevention by identifying at-risk individuals. However, the lack of domain-specific models, especially in low-resource languages, poses a significant challenge […]

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 […]

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 […]

Importance  Social determinants of health (SDOHs) are known to be associated with increased risk of suicidal behaviors, but few studies use SDOHs from unstructured electronic health record notes. Objective  To investigate associations between veterans’ death by suicide and recent SDOHs, identified using structured and unstructured data. Design, Setting, and Participants  This nested case-control study included veterans who received […]

(1) Introduction: Around a million people are reported to die by suicide every year, and due to the stigma associated with the nature of the death, this figure is usually assumed to be an underestimate. Machine learning and artificial intelligence such as natural language processing has the potential to become a major technique for the […]

Suicide is the 10th leading cause of death in the USA and globally. Despite decades of research, the ability to predict who will die by suicide is still no better than 50%. Traditional screening instruments have helped identify risk factors for suicide, but they have not provided accurate predictive power for reducing death rates. Over […]

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: In an age when telehealth services are increasingly being used for forward triage, there is a need for accurate suicide risk detection. Vocal characteristics analyzed using artificial intelligence are now proving capable of detecting suicide risk with accuracies superior to traditional survey-based approaches, suggesting an efficient and economical approach to ensuring ongoing patient safety. […]

Importance: Social determinants of health (SDOH) are known to be associated with increased risk of suicidal behaviors, but few studies utilized SDOH from unstructured electronic health record (EHR) notes. Objective: To investigate associations between suicide and recent SDOH, identified using structured and unstructured data. Design: Nested case-control study. Setting: EHR data from the US Veterans […]

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  […]

Objective Self-injurious thoughts and behaviors (SITBs) are a complex and enduring public health concern. Increasingly, teenagers use digital platforms to communicate about a range of mental health topics. These discussions may provide valuable information that can lead to insights about complex issues like SITBs. However, the field of clinical psychology currently lacks an easy-to-implement toolkit […]

Objective With early identification and intervention, many suicidal deaths are preventable. Tools that include machine learning methods have been able to identify suicidal language. This paper examines the persistence of this suicidal language up to 30 days after discharge from care. Method In a multi-center study, 253 subjects were enrolled into either suicidal or control cohorts. […]

We discuss computational language analysis as it pertains to suicide prevention research, with an emphasis on providing non-technologists with an understanding of key issues and, equally important, considering its relation to the broader enterprise of suicide prevention. Our emphasis here is on naturally occurring language in social media, motivated by its non-intrusive ability to yield […]

Objectives To improve the accuracy of classification of deaths of undetermined intent and to examine racial differences in misclassification. Methods We used natural language processing and statistical text analysis on restricted-access case narratives of suicides, homicides, and undetermined deaths in 37 states collected from the National Violent Death Reporting System (NVDRS) (2017). We fit separate […]

Introduction Although previous studies have consistently demonstrated that physicians are more likely than non-physicians to experience work-related stressors prior to suicide, the specific nature of these stressors remains unknown. The current study aimed to better characterize job-related problems prior to physician suicide. Methods The study utilized a mixed methods approach combining thematic analysis and natural […]

Objective We measured the frequency of clinicians’ assessments for access to lethal means, including firearms and medications in patients at risk of suicide from electronic medical and mental health records in outpatient and emergency settings. Methods We included adult patients who reported suicide ideation on the PHQ-9 depression screener in behavioral health and primary care […]