Uncovering Heterogeneous Associations of Disaster‐related Traumatic Experiences with Subsequent Mental Health Problems: a Machine Learning Approach

2021 ◽  
Author(s):  
Koichiro Shiba ◽  
Adel Daoud ◽  
Shiho Kino ◽  
Daisuke Nishi ◽  
Katsunori Kondo ◽  
...  
Keyword(s):  
Mental Health ◽  
Machine Learning ◽  
Mental Health Problems ◽  
Health Problems ◽  
Traumatic Experiences ◽  
Learning Approach ◽  
Machine Learning Approach

Safe but isolated – an interview study with Iraqi refugees in Sweden about social networks, social support, and mental health

2020 ◽  
pp. 002076402095425 ◽  
Author(s):  
Maria Sundvall ◽  
David Titelman ◽  
Valerie DeMarinis ◽  
Liubov Borisova ◽  
Önver Çetrez
Keyword(s):  
Mental Health ◽  
Social Support ◽  
Social Networks ◽  
Mental Health Problems ◽  
Health Assessment ◽  
Well Being ◽  
Health Problems ◽  
Traumatic Experiences ◽  
Iraqi Refugees ◽  
Structured Interviews

Background: Problems with social networks and social support are known to be associated with mental ill-health in refugees. Social support after migration promotes resilience. Aim: To study how Iraqi refugees who arrived in Sweden after the year 2000 perceived their social networks and social support, and to relate the observed network characteristics and changes to the refugees’ mental health and well-being. Method: Semi-structured interviews with 31 refugees, including questions on background and migration experiences, a biographical network map, and three health assessment scales. The findings were analysed with descriptive statistics and content thematic analysis. Results: The respondents’ networks were diminished. Social support was continued to be provided mainly by family members and supplemented by support from authorities. The main themes of the refugee experience of post-migration challenges were weakened social networks, barriers to integration and challenges to cultural and religious belonging. Failed reunion and worrying about relatives was described as particularly painful. Negative contacts with authority persons were often seen as humiliating or discriminating. Acquiring a new cultural belonging was described as challenging. At the same time, changing family and gender roles made it more difficult to preserve and develop the culture of origin. Traumatic experiences and mental health problems were common in this group. Family issues were more often than integration difficulties associated with mental health problems. Conclusion: In order to strengthen post-migration well-being and adaptation, authorities should support the refugees’ social networks. Clinicians need to address post-migration problems and challenges, including the meaning and function of social networks.

scholarly journals Using Machine Learning To Understand Suicide: A New Approach To Classifying Australian Coroner’s Court Decisions

2021 ◽  
Author(s):  
Ravi Iyer ◽  
Elizabeth Seabrook ◽  
Suku Sukunesan ◽  
Maja Nedeljkovic ◽  
Denny Meyer
Keyword(s):  
Mental Health ◽  
Machine Learning ◽  
Mental Health Disorder ◽  
Supervised Machine Learning ◽  
Feature Engineering ◽  
Court Case ◽  
Learning Approach ◽  
Machine Learning Classification ◽  
Machine Learning Approach ◽  
Case Files

Abstract We aimed to demonstrate how a large collection of publicly accessible Australian Coroner’s Court case files (n=4459) (2009-2019) can be automatically classified for determination of death by suicide, presence of mental health disorder and sex of deceased via Natural Language Processing (NLP) methods - supervised machine learning and unsupervised dictionary-based and string search based approaches. We achieved superior levels of accuracy in the machine learning classification (Gradient Boosting vs. Random Forest baseline) of deaths by suicide of 83.3% (sensitivity = 85.1%, Specificity = 79.1%) and an accuracy of 98.3% for the dictionary-based classification of mental health disorder, as defined by the OCD-10 (sensitivity = 99.0%, specificity = 97.9%). Our machine learning approach automatically classified 24.2% (1078/4459) of the case files as referring to deaths by suicide while 63.7% (2940/4459) where classified as exhibiting a mental health disorder1. We employed a two-stage machine learning approach involving feature engineering, followed by predictive modelling in the second. Feature engineering involved several steps including removal of low value text, parts of speech analysis, term document weighting and topic clustering. Predictive classification involved extensive hyperparameter tuning to yield the most accurate model. We validated our models against a manually pre-coded subsample of case files, and also via binary logistic regression to test the contribution of each classified mental health disorder against determinations of deaths by suicide according to extant literature. This validation step confirmed elevated odds of suicide attributed to diagnoses of Depression, Schizophrenia and Obsessive Compulsive Disorder. Finally, we offer a short case study to demonstrate the efficacy of our approach in investigating a subset of case findings referring to suicides resulting from family violence. We offer a proof of concept model that demonstrates an objective and scalable approach to the analysis of legal texts. The use of NLP methods in analysing Coroner's Court case findings has important implications for the ongoing development of a real-time surveillance of suicide system in Australia.

scholarly journals Vulnerability and Protective Factors for PTSD and Depression Symptoms Among Healthcare Workers During COVID-19: A Machine Learning Approach

2022 ◽  
Vol 12 ◽  
Author(s):  
Liana C. L. Portugal ◽  
Camila Monteiro Fabricio Gama ◽  
Raquel Menezes Gonçalves ◽  
Mauro Vitor Mendlowicz ◽  
Fátima Smith Erthal ◽  
...  
Keyword(s):  
Mental Health ◽  
Machine Learning ◽  
Social Isolation ◽  
Healthcare Workers ◽  
Intervention Programs ◽  
Depression Symptoms ◽  
Learning Approach ◽  
Professional Recognition ◽  
Machine Learning Approach

Background: Healthcare workers are at high risk for developing mental health problems during the COVID-19 pandemic. There is an urgent need to identify vulnerability and protective factors related to the severity of psychiatric symptoms among healthcare workers to implement targeted prevention and intervention programs to reduce the mental health burden worldwide during COVID-19.Objective: The present study aimed to apply a machine learning approach to predict depression and PTSD symptoms based on psychometric questions that assessed: (1) the level of stress due to being isolated from one's family; (2) professional recognition before and during the pandemic; and (3) altruistic acceptance of risk during the COVID-19 pandemic among healthcare workers.Methods: A total of 437 healthcare workers who experienced some level of isolation at the time of the pandemic participated in the study. Data were collected using a web survey conducted between June 12, 2020, and September 19, 2020. We trained two regression models to predict PTSD and depression symptoms. Pattern regression analyses consisted of a linear epsilon-insensitive support vector machine (ε-SVM). Predicted and actual clinical scores were compared using Pearson's correlation coefficient (r), the coefficient of determination (r2), and the normalized mean squared error (NMSE) to evaluate the model performance. A permutation test was applied to estimate significance levels.Results: Results were significant using two different cross-validation strategies to significantly decode both PTSD and depression symptoms. For all of the models, the stress due to social isolation and professional recognition were the variables with the greatest contributions to the predictive function. Interestingly, professional recognition had a negative predictive value, indicating an inverse relationship with PTSD and depression symptoms.Conclusions: Our findings emphasize the protective role of professional recognition and the vulnerability role of the level of stress due to social isolation in the severity of posttraumatic stress and depression symptoms. The insights gleaned from the current study will advance efforts in terms of intervention programs and public health messaging.

scholarly journals Mental health and resilience during the coronavirus pandemic: A machine learning approach

2021 ◽  
Author(s):  
Kristin W. Samuelson ◽  
Kelly Dixon ◽  
Joshua T. Jordan ◽  
Tyler Powers ◽  
Samantha Sonderman ◽  
...  
Keyword(s):  
Mental Health ◽  
Machine Learning ◽  
Learning Approach ◽  
Machine Learning Approach

scholarly journals Combining International Survey Datasets to Identify Indicators of Stress during the COVID-19 Pandemic: A Machine Learning Approach to Improve Generalization

COVID ◽  
2021 ◽  
Vol 1 (4) ◽  
pp. 728-738
Author(s):  
Eric Yunan Zhao ◽  
Daniel Xia ◽  
Mark Greenhalgh ◽  
Elena Colicino ◽  
Merylin Monaro ◽  
...  
Keyword(s):  
Mental Health ◽  
Machine Learning ◽  
Psychological Stress ◽  
Mental Health Problems ◽  
Health Problems ◽  
International Studies ◽  
Long Term Effects ◽  
Applied Machine Learning ◽  
Drastic Increase ◽  
And Gender

The scale and duration of the worldwide SARS-COVID-2 virus-related quarantine measures presented the global scientific community with a unique opportunity to study the accompanying psychological stress. Since March 2020, numerous publications have reported similar findings from diverse international studies on psychological stress, depression, and anxiety, which have increased during this pandemic. However, there remains a gap in interpreting the results from one country to another despite the global rise in mental health problems. The objective of our study was to identify global indicators of pandemic-related stress that traverse geographic and cultural boundaries. We amalgamated data from two independent global surveys across twelve countries and spanning four continents collected during the first wave of the mandated public health measures aimed at mitigating COVID-19. We applied machine learning (ML) modelling to these data, and the results revealed a significant positive correlation between PSS-10 scores and gender, relationship status, and groups. Confinement, fear of contagion, social isolation, financial hardship, etc., may be some reasons reported being the cause of the drastic increase in mental health problems worldwide. The decline of the typical protective factors (e.g., sleep, exercise, meditation) may have amplified existing vulnerabilities/co-morbidities (e.g., psychiatric history, age, gender). Our results further show that ML is an apropos tool to elucidate the underlying predictive factors in large, complex, heterogeneous datasets without invalidating the model assumptions. We believe our model provides clinicians, researchers, and decision-makers with evidence to investigate the moderators and mediators of stress and introduce novel interventions to mitigate the long-term effects of the COVID-19 pandemic.

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