scholarly journals Pandora’s Bot: Insights from the Syntax and Semantics of Suicide Notes

2021 ◽  
Author(s):  
David Ireland ◽  
Dana Kai Bradford
Keyword(s):  
Mental Health ◽  
Natural Language Processing ◽  
Language Processing ◽  
High Rate ◽  
Physical And Mental Health ◽  
Health And Wellbeing ◽  
Suicide Notes ◽  
Health Distress ◽  
Mental Health Distress ◽  
Chat Bot

Conversation agents (chat-bots) are becoming ubiquitous in many domains of everyday life, including physical and mental health and wellbeing. With the high rate of suicide in Australia, chat-bot developers are facing the challenge of dealing with statements related to mental ill-health, depression and suicide. Advancements in natural language processing could allow for sensitive, considered responses, provided suicidal discourse can be accurately detected. Here suicide notes are examined for consistent linguistic syntax and semantic patterns used by individuals in mental health distress. Paper contains distressing content.

scholarly journals Suicide Note Classification Using Natural Language Processing: A Content Analysis

2010 ◽  
Vol 3 ◽  
pp. BII.S4706 ◽  
Author(s):  
John Pestian ◽  
Henry Nasrallah ◽  
Pawel Matykiewicz ◽  
Aurora Bennett ◽  
Antoon Leenaars
Keyword(s):  
Mental Health ◽  
Machine Learning ◽  
Natural Language Processing ◽  
Natural Language ◽  
Cause Of Death ◽  
Language Processing ◽  
Mental Health Professionals ◽  
Health Professionals ◽  
Machine Learning Algorithms ◽  
Suicide Notes

Suicide is the second leading cause of death among 25–34 year olds and the third leading cause of death among 15–25 year olds in the United States. In the Emergency Department, where suicidal patients often present, estimating the risk of repeated attempts is generally left to clinical judgment. This paper presents our second attempt to determine the role of computational algorithms in understanding a suicidal patient's thoughts, as represented by suicide notes. We focus on developing methods of natural language processing that distinguish between genuine and elicited suicide notes. We hypothesize that machine learning algorithms can categorize suicide notes as well as mental health professionals and psychiatric physician trainees do. The data used are comprised of suicide notes from 33 suicide completers and matched to 33 elicited notes from healthy control group members. Eleven mental health professionals and 31 psychiatric trainees were asked to decide if a note was genuine or elicited. Their decisions were compared to nine different machine-learning algorithms. The results indicate that trainees accurately classified notes 49% of the time, mental health professionals accurately classified notes 63% of the time, and the best machine learning algorithm accurately classified the notes 78% of the time. This is an important step in developing an evidence-based predictor of repeated suicide attempts because it shows that natural language processing can aid in distinguishing between classes of suicidal notes.

scholarly journals Analysis of mental and physical disorders associated with COVID-19 in online health forums: a natural language processing study

BMJ Open ◽  
2021 ◽  
Vol 11 (11) ◽  
pp. e056601
Author(s):  
Rashmi Patel ◽  
Fabrizio Smeraldi ◽  
Maryam Abdollahyan ◽  
Jessica Irving ◽  
Conrad Bessant
Keyword(s):  
Mental Health ◽  
Natural Language Processing ◽  
Natural Language ◽  
Language Processing ◽  
Data Extraction ◽  
Population Level ◽  
Physical And Mental Health ◽  
Time Data ◽  
Public Response ◽  
Physical Disorders

ObjectivesOnline health forums provide rich and untapped real-time data on population health. Through novel data extraction and natural language processing (NLP) techniques, we characterise the evolution of mental and physical health concerns relating to the COVID-19 pandemic among online health forum users.Setting and designWe obtained data from three leading online health forums: HealthBoards, Inspire and HealthUnlocked, from the period 1 January 2020 to 31 May 2020. Using NLP, we analysed the content of posts related to COVID-19.Primary outcome measures(1) Proportion of forum posts containing COVID-19 keywords; (2) proportion of forum users making their very first post about COVID-19; (3) proportion of COVID-19-related posts containing content related to physical and mental health comorbidities.ResultsData from 739 434 posts created by 53 134 unique users were analysed. A total of 35 581 posts (4.8%) contained a COVID-19 keyword. Posts discussing COVID-19 and related comorbid disorders spiked in early March to mid-March around the time of global implementation of lockdowns prompting a large number of users to post on online health forums for the first time. Over a quarter of COVID-19-related thread titles mentioned a physical or mental health comorbidity.ConclusionsWe demonstrate that it is feasible to characterise the content of online health forum user posts regarding COVID-19 and measure changes over time. The pandemic and corresponding public response has had a significant impact on posters’ queries regarding mental health. Social media data sources such as online health forums can be harnessed to strengthen population-level mental health surveillance.

Prevalence of Self-Reported Depression Symptoms and Perceived Anxiety Among Community-Dwelling U.S. Adults Reporting Tinnitus

2020 ◽  
Vol 5 (4) ◽  
pp. 959-970
Author(s):  
Kelly M. Reavis ◽  
James A. Henry ◽  
Lynn M. Marshall ◽  
Kathleen F. Carlson
Keyword(s):  
Mental Health ◽  
Fold Increase ◽  
Community Dwelling ◽  
Self Report ◽  
Depression Symptoms ◽  
Community Based Interventions ◽  
Data Set ◽  
Health Distress ◽  
Validated Questionnaire ◽  
Mental Health Distress

Purpose The aim of this study was to examine the relationship between tinnitus and self-reported mental health distress, namely, depression symptoms and perceived anxiety, in adults who participated in the National Health and Nutrition Examinations Survey between 2009 and 2012. A secondary aim was to determine if a history of serving in the military modified the associations between tinnitus and mental health distress. Method This was a cross-sectional study design of a national data set that included 5,550 U.S. community-dwelling adults ages 20 years and older, 12.7% of whom were military Veterans. Bivariable and multivariable logistic regression was used to estimate the association between tinnitus and mental health distress. All measures were based on self-report. Tinnitus and perceived anxiety were each assessed using a single question. Depression symptoms were assessed using the Patient Health Questionnaire, a validated questionnaire. Multivariable regression models were adjusted for key demographic and health factors, including self-reported hearing ability. Results Prevalence of tinnitus was 15%. Compared to adults without tinnitus, adults with tinnitus had a 1.8-fold increase in depression symptoms and a 1.5-fold increase in perceived anxiety after adjusting for potential confounders. Military Veteran status did not modify these observed associations. Conclusions Findings revealed an association between tinnitus and both depression symptoms and perceived anxiety, independent of potential confounders, among both Veterans and non-Veterans. These results suggest, on a population level, that individuals with tinnitus have a greater burden of perceived mental health distress and may benefit from interdisciplinary health care, self-help, and community-based interventions. Supplemental Material https://doi.org/10.23641/asha.12568475

Socio Demographic and Clinical Profile of Indoor Female Patients of RINPAS

2017 ◽  
Vol 8 (1) ◽  
pp. 33
Author(s):  
Rajni Suri ◽  
Anshu Suri ◽  
Neelam Kumari ◽  
Amool R. Singh ◽  
Manisha Kiran
Keyword(s):  
Mental Health ◽  
Mental Illness ◽  
Mentally Ill ◽  
Mental Health Problems ◽  
Demographic Data ◽  
Relevant Information ◽  
Health Problems ◽  
High Rate ◽  
Physical And Mental Health ◽  
Cross Sectional

The role of women is very crucial in our society. She cares for her parents, partner, children and other relatives. She performs all types of duties in family and also in the society without any expectations. Because of playing many roles, women often face many challenges in their life including both physical and mental. Mental health problems affect women and men equally, but some problems are more common among women including both physical and mental health problems. Aim of the study - The present study is aimed to describe and compare the clinical and socio-demographic correlates of female mentally ill patients. Methods and Materials: The study includes 180 female mentally ill patients based on cross sectional design and the sample for the study was drawn purposively. A semi structured socio-demographic data sheet was prepared to collect relevant information as per the need of the study. Result: The present study reveals that the socio-demographic factors contribute a vital role in mental illness. Findings also showed that majority of patients had mental problems in the age range of 20-30 have high rate. Illiterate and primary level of education and daily wage working women as well as low and middle socio-economic status women are more prone to have mental illness. Other factors like marital status, type of family and religion etc also important factors for mental illness. Keywords: Socio demographic profile, female, psychiatric patient

scholarly journals A Risk Exchange: Health and Mobility in the Context of Climate and Environmental Change in Bangladesh—A Qualitative Study

2021 ◽  
Vol 18 (5) ◽  
pp. 2629
Author(s):  
Patricia Nayna Schwerdtle ◽  
Kate Baernighausen ◽  
Sayeda Karim ◽  
Tauheed Syed Raihan ◽  
Samiya Selim ◽  
...  
Keyword(s):  
Climate Change ◽  
Mental Health ◽  
Quality Of Care ◽  
Health Risks ◽  
Thematic Analysis ◽  
Health Determinants ◽  
Physical And Mental Health ◽  
Health And Wellbeing ◽  
Healthcare Environment

Background: Climate change influences patterns of human mobility and health outcomes. While much of the climate change and migration discourse is invested in quantitative predictions and debates about whether migration is adaptive or maladaptive, less attention has been paid to the voices of the people moving in the context of climate change with a focus on their health and wellbeing. This qualitative research aims to amplify the voices of migrants themselves to add nuance to dominant migration narratives and to shed light on the real-life challenges migrants face in meeting their health needs in the context of climate change. Methods: We conducted 58 semi-structured in-depth interviews with migrants purposefully selected for having moved from rural Bhola, southern Bangladesh to an urban slum in Dhaka, Bangladesh. Transcripts were analysed using thematic analysis under the philosophical underpinnings of phenomenology. Coding was conducted using NVivo Pro 12. Findings: We identified two overarching themes in the thematic analysis: Firstly, we identified the theme “A risk exchange: Exchanging climate change and health risks at origin and destination”. Rather than describing a “net positive” or “net negative” outcome in terms of migration in the context of climate change, migrants described an exchange of hazards, exposures, and vulnerabilities at origin with those at destination, which challenged their capacity to adapt. This theme included several sub-themes—income and employment factors, changing food environment, shelter and water sanitation and hygiene (WaSH) conditions, and social capital. The second overarching theme was “A changing health and healthcare environment”. This theme also included several sub-themes—changing physical and mental health status and a changing healthcare environment encompassing quality of care and barriers to accessing healthcare. Migrants described physical and mental health concerns and connected these experiences with their new environment. These two overarching themes were prevalent across the dataset, although each participant experienced and expressed them uniquely. Conclusion: Migrants who move in the context of climate change face a range of diverse health risks at the origin, en route, and at the destination. Migrating individuals, households, and communities undertake a risk exchange when they decide to move, which has diverse positive and negative consequences for their health and wellbeing. Along with changing health determinants is a changing healthcare environment where migrants face different choices, barriers, and quality of care. A more migrant-centric perspective as described in this paper could strengthen migration, climate, and health governance. Policymakers, urban planners, city corporations, and health practitioners should integrate the risk exchange into practice and policies.

Using Natural Language Processing to Assess the Psychological Effect of COVID-19 Pandemic on Insomnia via Tweets: A Pre-Post Retrospective Pilot Study (Preprint)

2021 ◽  
Author(s):  
Arash Maghsoudi ◽  
Sara Nowakowski ◽  
Ritwick Agrawal ◽  
Amir Sharafkhaneh ◽  
Sadaf Aram ◽  
...  
Keyword(s):  
Mental Health ◽  
Social Media ◽  
Natural Language Processing ◽  
Natural Language ◽  
Language Processing ◽  
Temporal Analysis ◽  
Health Condition ◽  
Additional Stress ◽  
Mental Health Condition ◽  
Twitter Users

BACKGROUND The COVID-19 pandemic has imposed additional stress on population health that may result in a higher incidence of insomnia. In this study, we hypothesized that using natural language processing (NLP) to explore social media would help to identify the mental health condition of the population experiencing insomnia after the outbreak of COVID-19. OBJECTIVE In this study, we hypothesized that using natural language processing (NLP) to explore social media would help to identify the mental health condition of the population experiencing insomnia after the outbreak of COVID-19. METHODS We designed a pre-post retrospective study using public social media content from Twitter. We categorized tweets based on time into two intervals: prepandemic (01/01/2019 to 01/01/2020) and pandemic (01/01/2020 to 01/01/2021). We used NLP to analyze polarity (positive/negative) and intensity of emotions and also users’ tweets psychological states in terms of sadness, anxiety and anger by counting the words related to these categories in each tweet. Additionally, we performed temporal analysis to examine the effect of time on the users’ insomnia experience. RESULTS We extracted 268,803 tweets containing the word insomnia (prepandemic, 123,293 and pandemic, 145,510). The odds of negative tweets (OR, 1.31; 95% CI, 1.29-1.33), anger (OR, 1.19; 95% CI, 1.16-1.21), and anxiety (OR, 1.24; 95% CI: 1.21-1.26) were higher during the pandemic compared to prepandemic. The likelihood of negative tweets after midnight was higher than for other daily intevals, comprising approximately 60% of all negative insomnia-related tweets in 2020 and 2021 collectively. CONCLUSIONS Twitter users shared more negative tweets about insomnia during the pandemic than during the year before. Also, more anger and anxiety-related content were disseminated during the pandemic on the social media platform. Future studies using an NLP framework could assess tweets about other psychological distress, habit changes, weight gain due to inactivity, and the effect of viral infection on sleep.

scholarly journals Causal Factors of Effective Psychosocial Outcomes in Online Mental Health Communities

2020 ◽  
Author(s):  
Koustuv Saha ◽  
Amit Sharma
Keyword(s):  
Mental Health ◽  
Online Community ◽  
Psychosocial Outcomes ◽  
Case Control ◽  
Case Control Studies ◽  
Causal Factors ◽  
Health Distress ◽  
Health Communities ◽  
Mental Health Distress ◽  
And Control

Online mental health communities enable people to seek and provide support, and growing evidence shows the efficacy of community participation to cope with mental health distress. However, what factors of peer support lead to favorable psychosocial outcomes for individuals is less clear. Using a dataset of over 300K posts by ∼39K individuals on an online community TalkLife, we present a study to investigate the effect of several factors, such as adaptability, diversity, immediacy, and the nature of support. Unlike typical causal studies that focus on the effect of each treatment, we focus on the outcome and address the reverse causal question of identifying treatments that may have led to the outcome, drawing on case-control studies in epidemiology. Specifically, we define the outcome as an aggregate of affective, behavioral, and cognitive psychosocial change and identify Case (most improved) and Control (least improved) cohorts of individuals. Considering responses from peers as treatments, we evaluate the differences in the responses received by Case and Control, per matched clusters of similar individuals. We find that effective support includes complex language factors such as diversity, adaptability, and style, but simple indicators such as quantity and immediacy are not causally relevant. Our work bears methodological and design implications for online mental health platforms, and has the potential to guide suggestive interventions for peer supporters on these platforms.

scholarly journals A Pipeline to Understand Emerging Illness Via Social Media Data Analysis: Case Study on Breast Implant Illness (Preprint)

2021 ◽  
Author(s):  
Vishal Dey ◽  
Peter Krasniak ◽  
Minh Nguyen ◽  
Clara Lee ◽  
Xia Ning
Keyword(s):  
Mental Health ◽  
Social Media ◽  
Natural Language Processing ◽  
Data Analysis ◽  
Natural Language ◽  
Language Processing ◽  
Breast Implant ◽  
Public Attention ◽  
Social Media Data ◽  
Media Data

BACKGROUND A new illness can come to public attention through social media before it is medically defined, formally documented, or systematically studied. One example is a condition known as breast implant illness (BII), which has been extensively discussed on social media, although it is vaguely defined in the medical literature. OBJECTIVE The objective of this study is to construct a data analysis pipeline to understand emerging illnesses using social media data and to apply the pipeline to understand the key attributes of BII. METHODS We constructed a pipeline of social media data analysis using natural language processing and topic modeling. Mentions related to signs, symptoms, diseases, disorders, and medical procedures were extracted from social media data using the clinical Text Analysis and Knowledge Extraction System. We mapped the mentions to standard medical concepts and then summarized these mapped concepts as topics using latent Dirichlet allocation. Finally, we applied this pipeline to understand BII from several BII-dedicated social media sites. RESULTS Our pipeline identified topics related to toxicity, cancer, and mental health issues that were highly associated with BII. Our pipeline also showed that cancers, autoimmune disorders, and mental health problems were emerging concerns associated with breast implants, based on social media discussions. Furthermore, the pipeline identified mentions such as rupture, infection, pain, and fatigue as common self-reported issues among the public, as well as concerns about toxicity from silicone implants. CONCLUSIONS Our study could inspire future studies on the suggested symptoms and factors of BII. Our study provides the first analysis and derived knowledge of BII from social media using natural language processing techniques and demonstrates the potential of using social media information to better understand similar emerging illnesses. CLINICALTRIAL

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