When do traumatic experiences alter risk-taking behavior? A machine learning analysis of reports from refugees

Augsburger, Mareike; Elbert, Thomas

doi:10.1371/journal.pone.0177617

Cited by 21 publications

(19 citation statements)

References 44 publications

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“…The Patient Health Questionnaire–9 (PHQ‐9; Kroenke, Spitzer, & Williams, ) was used to assess depressive symptoms over the course of the preceding 2 weeks. The PHQ‐9 has excellent psychometric properties and has previously been used to screen for depression in refugee populations (Augsburger & Elbert, ). Participants were asked to rate items on a scale ranging from 0 ( not at all ) to 3 ( nearly every day ).…”

Section: Methodsmentioning

confidence: 99%

Differential Associations Among PTSD and Complex PTSD Symptoms and Traumatic Experiences and Postmigration Difficulties in a Culturally Diverse Refugee Sample

Hecker

Huber

Maier

et al. 2018

Journal of Traumatic Stress

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Forced migration is one of the major challenges currently facing the international community. Many refugees have been affected by traumatic experiences at home and during their flight, putting them at a heightened risk of developing trauma‐related disorders. The new version of the International Classification of Diseases (ICD‐11) introduced two sibling disorders, posttraumatic stress disorder (PTSD) and complex PTSD (CPTSD). So far, little is known about risk and protective factors in refugees that are specifically associated with the disturbances in self‐organization (DSO) characteristic of CPTSD. In this study, we aimed to investigate the association between PTSD and DSO symptoms and traumatic experiences, postmigration difficulties, and social support in a culturally diverse sample of refugees who resettled in Switzerland. A total of 94 refugees (85.1% male; M age = 31.60 years, SD = 10.14, range: 18–61 years) participated in this study. Trained assessors performed either guided questionnaire assessments or structured interviews. In our advice‐ and help‐seeking sample, 32.9% of individuals suffered from PTSD and 21.3% from CPTSD. After controlling for potential gender differences, we found positive associations between PTSD symptoms and trauma exposure, β = .22, as well as between DSO symptoms and postmigration living difficulties, β = .42, and lack of social support, β = .22. Our findings support the notion that it is highly important to consider differential associations among PTSD and DSO symptoms and risk and protective factors to gain a deeper understanding of the trauma‐related problems refugees face.

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Section: Methodsmentioning

confidence: 99%

Differential Associations Among PTSD and Complex PTSD Symptoms and Traumatic Experiences and Postmigration Difficulties in a Culturally Diverse Refugee Sample

Hecker

Huber

Maier

et al. 2018

Journal of Traumatic Stress

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“…Refugees were either referred to the CEP by social workers, physicians, volunteers, or lawyers, or they were recruited from asylum accommodations in southern Germany. All were initially invited to a structured clinical interview for the assessment of the trauma history and mental health status in the context of other studies [52][53][54]. In case of mental health problems, the participant and the psychologist decided after the interview how to proceed.…”

Section: Design and Proceduresmentioning

confidence: 99%

Trajectories of posttraumatic stress symptoms during and after Narrative Exposure Therapy (NET) in refugees

et al. 2020

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Background: Trauma-focused therapy approaches are recommended as treatment for posttraumatic stress disorder (PTSD). This includes the treatment of trauma-related suffering in refugee populations. However, there is a lack of knowledge about symptom trajectories in refugees living in volatile conditions. This has led to fear of "retraumatisation" and general skepticism in clinicians concerning the use of exposure therapy. Methods: To test the relevance of this concern, we investigated PTSD symptom trajectories and potentially influencing factors during the course of Narrative Exposure Therapy (NET) in a refugee sample living in Germany. Refugees filled out the PTSD Checklist prior to each treatment session and also during follow-up interviews. Therapists continuously documented positive and negative life events as well as the content of the treatment sessions. Additionally, structured clinical interviews were conducted pre-treatment and at follow-up time points. Results: On average, clients presented with substantial decreases in PTSD symptoms already during and after NET. However, symptom trajectories differed and ranged from fast responders to slow responders to no immediate response during treatment. Importantly, a persistent worsening of symptoms was not observed, also not after exposure to the most distressing events. In contrast, stressful life experiences seemed to aggravate PTSD symptoms. Conclusions: Consistent with earlier studies, NET leads to clinically and behaviorally relevant reductions in PTSD symptoms both throughout and following treatment in refugees living in volatile conditions. Concerns about imaginal exposure in refugees were not substantiated. While stressful life events contributed to transient symptom increases, they weren't found to prevent the overall effectiveness of NET. Trial registration: NCT02852616.

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“…Augsburger and Elbert [32] performed a ML technique to predict risk-taking behavior for refugees that have experienced traumatic experiences. Data sets consisted of Balloon analogue risk task (BART) and questionnaires in interviews of 56 cases.…”

Section: Diagnosis Of Health Diseases By Using Different Machine Learmentioning

confidence: 99%

Review of Machine Learning Algorithms for Diagnosing Mental Illness

Cho

Yim

Choi

et al. 2019

Psychiatry Investig

176

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Objective Enhanced technology in computer and internet has driven scale and quality of data to be improved in various areas including healthcare sectors. Machine Learning (ML) has played a pivotal role in efficiently analyzing those big data, but a general misunderstanding of ML algorithms still exists in applying them (e.g., ML techniques can settle a problem of small sample size, or deep learning is the ML algorithm). This paper reviewed the research of diagnosing mental illness using ML algorithm and suggests how ML techniques can be employed and worked in practice. Methods Researches about mental illness diagnostic using ML techniques were carefully reviewed. Five traditional ML algorithms-Support Vector Machines (SVM), Gradient Boosting Machine (GBM), Random Forest, Naïve Bayes, and K-Nearest Neighborhood (KNN)-frequently used for mental health area researches were systematically organized and summarized. Results Based on literature review, it turned out that Support Vector Machines (SVM), Gradient Boosting Machine (GBM), Random Forest, Naïve Bayes, and K-Nearest Neighborhood (KNN) were frequently employed in mental health area, but many researchers did not clarify the reason for using their ML algorithm though every ML algorithm has its own advantages. In addition, there were several studies to apply ML algorithms without fully understanding the data characteristics. Conclusion Researchers using ML algorithms should be aware of the properties of their ML algorithms and the limitation of the results they obtained under restricted data conditions. This paper provides useful information of the properties and limitation of each ML algorithm in the practice of mental health.

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When do traumatic experiences alter risk-taking behavior? A machine learning analysis of reports from refugees

Cited by 21 publications

References 44 publications

Differential Associations Among PTSD and Complex PTSD Symptoms and Traumatic Experiences and Postmigration Difficulties in a Culturally Diverse Refugee Sample

Differential Associations Among PTSD and Complex PTSD Symptoms and Traumatic Experiences and Postmigration Difficulties in a Culturally Diverse Refugee Sample

Trajectories of posttraumatic stress symptoms during and after Narrative Exposure Therapy (NET) in refugees

Review of Machine Learning Algorithms for Diagnosing Mental Illness

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