How do healthcare professionals interview patients to assess suicide risk?

Background: There is little evidence on how professionals communicate to assess suicide risk. This study analysed how professionals interview patients about suicidal ideation in clinical practice. Methods: Three hundred nineteen video-recorded outpatient visits in U.K. secondary mental health care were screened. 83 exchanges about suicidal ideation were identified in 77 visits. A convenience sample of 6 cases in 46 primary care visits was also analysed. Depressive symptoms were assessed. Questions and responses were qualitatively analysed using conversation analysis. χ 2 tested whether questions were influenced by severity of depression or influenced patients’ responses. Results: A gateway closed question was always asked inviting a yes/no response. 75% of questions were negatively phrased, communicating an expectation of no suicidal ideation, e.g., “No thoughts of harming yourself?”. 25% were positively phrased, communicating an expectation of suicidal ideation, e.g., “Do you feel life is not worth living?”. Comparing these two question types, patients were significantly more likely to say they were not suicidal when the question was negatively phrased but were not more likely to say they were suicidal when positively phrased (χ 2 = 7.2, df = 1, p = 0.016). 25% patients responded with a narrative rather than a yes/no, conveying ambivalence. Here, psychiatrists tended to pursue a yes/no response. When the patient responded no to the gateway question, the psychiatrist moved on to the next topic. A similar pattern was identified in primary care. Conclusions: Psychiatrists tend to ask patients to confirm they are not suicidal using negative questions. Negatively phrased questions bias patients’ responses towards reporting no suicidal ideation

Profiling a set of personality traits of text author: what our words reveal about us

Authorship profiling, i.e. revealing information about an unknown author by analyzing their text, is a task of growing importance. One of the most urgent problems of authorship profiling (AP) is selecting text parameters which may correlate to an author’s personality. Most researchers’ selection of these is not underpinned by any theory. This article proposes an approach to AP which applies neuroscience data. The aim of the study is to assess the probability of self-destructive behaviour of an individual via formal parameters of their texts. Here we have used the “Personality Corpus”, which consists of Russian-language texts. A set of correlations between scores on the Freiburg Personality Inventory scales that are known to be indicative of self-destructive behaviour (“Spontaneous Aggressiveness”, “Depressiveness”, “Emotional Lability”, and “Composedness”) and text variables (average sentence length, lexical diversity etc.) has been calculated. Further, a mathematical model which predicts the probability of self-destructive behaviour has been obtained

The 'At-risk mental state' for psychosis in adolescents : clinical presentation, transition and remission.

Despite increased efforts over the last decade to prospectively identify individuals at ultra-high risk of developing a psychotic illness, limited attention has been specifically directed towards adolescent populations (<18 years). In order to evaluate how those under 18 fulfilling the operationalised criteria for an At-Risk Mental State (ARMS) present and fare over time, we conducted an observational study. Participants (N = 30) generally reported a high degree of functional disability and frequent and distressing perceptual disturbance, mainly in the form of auditory hallucinations. Seventy percent (21/30) were found to fulfil the criteria for a co-morbid ICD-10 listed mental health disorder, with mood (affective; 13/30) disorders being most prevalent. Overall transition rates to psychosis were low at 24 months follow-up (2/28; 7.1 %) whilst many participants demonstrated a significant reduction in psychotic-like symptoms. The generalisation of these findings may be limited due to the small sample size and require replication in a larger sample

Artificial Intelligence for Suicide Assessment using Audiovisual Cues: A Review

Death by suicide is the seventh leading death cause worldwide. The recent advancement in Artificial Intelligence (AI), specifically AI applications in image and voice processing, has created a promising opportunity to revolutionize suicide risk assessment. Subsequently, we have witnessed fast-growing literature of research that applies AI to extract audiovisual non-verbal cues for mental illness assessment. However, the majority of the recent works focus on depression, despite the evident difference between depression symptoms and suicidal behavior and non-verbal cues. This paper reviews recent works that study suicide ideation and suicide behavior detection through audiovisual feature analysis, mainly suicidal voice/speech acoustic features analysis and suicidal visual cues. Automatic suicide assessment is a promising research direction that is still in the early stages. Accordingly, there is a lack of large datasets that can be used to train machine learning and deep learning models proven to be effective in other, similar tasks.Comment: Manuscript submitted to Arificial Intelligence Reviews (2022

Research Advances: January 2014

The VA has a comprehensive research agenda to help the newest generation of Veterans -- those returning from operations Enduring Freedom, Iraqi Freedom, and New Dawn. In addition to exploring new treatments for traumatic brain injury and other complex blast-related injuries, VA researchers are examining ways to improve the delivery of health care services for these Veterans and promote their reintegration back into their families, communities, and workplaces.This publication reviews recent advances in research about Veterans' health and well-being

The Verbal and Non Verbal Signals of Depression -- Combining Acoustics, Text and Visuals for Estimating Depression Level

Depression is a serious medical condition that is suffered by a large number of people around the world. It significantly affects the way one feels, causing a persistent lowering of mood. In this paper, we propose a novel attention-based deep neural network which facilitates the fusion of various modalities. We use this network to regress the depression level. Acoustic, text and visual modalities have been used to train our proposed network. Various experiments have been carried out on the benchmark dataset, namely, Distress Analysis Interview Corpus - a Wizard of Oz (DAIC-WOZ). From the results, we empirically justify that the fusion of all three modalities helps in giving the most accurate estimation of depression level. Our proposed approach outperforms the state-of-the-art by 7.17% on root mean squared error (RMSE) and 8.08% on mean absolute error (MAE).Comment: 10 pages including references, 2 figure

Libraries of life: Using life history books with depressed care home residents

Depression is a common, and often undetected, psychiatric disorder in geriatric care home residents 1,2. Reminiscence, an independent nursing therapy used by a variety of health and social care professionals, can prevent or reduce depression 14. This practice development project explored the use of reminiscence life history books as an interpersonal therapeutic tool with three depressed care home residents living in residential care and skilled nursing facilities. The process of choosing to produce a book, assessment of capabilities, and methods of construction are described using three illustrative case studies. Three themes emerged: reviewing the past, accepting the present, and dreaming of an alternative future. This project demonstrated that life history books, tailored to individual needs and abilities, can facilitate reminiscence and reduce depression by increasing social interaction. The benefits to residents, their families and care staff are discussed and the relevance to nursing practice highlighted

The Double-edged Sword: A Mixed Methods Study of the Interplay between Bipolar Disorder and Technology Use

Human behavior is increasingly reflected or acted out through technology. This is of particular salience when it comes to changes in behavior associated with serious mental illnesses including schizophrenia and bipolar disorder. Early detection is crucial for these conditions but presently very challenging to achieve. Potentially, characteristics of these conditions\u27 traits and symptoms, at both idiosyncratic and collective levels, may be detectable through technology use patterns. In bipolar disorder specifically, initial evidence associates changes in mood with changes in technology-mediated communication patterns. However much less is known about how people with bipolar disorder use technology more generally in their lives, how they view their technology use in relation to their illness, and, perhaps most crucially, the causal relationship (if any exists) between their technology use and their disease. To address these uncertainties, we conducted a survey of people with bipolar disorder (N = 84). Our results indicate that technology use varies markedly with changes in mood and that technology use broadly may have potential as an early warning signal of mood episodes. We also find that technology for many of these participants is a double-edged sword: acting as both a culprit that can trigger or exacerbate symptoms as well as a support mechanism for recovery. These findings have implications for the design of both early warning systems and technology-mediated interventions