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Stress Disorders, Post-Traumatic

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[AI based Evaluation of Psychotrauma related to Lahars in the Commune of PrĂȘcheur in the French Antilles].

Sante mentale au Quebec
Objectives Natural disasters have a significant impact on mental health. Data collected from the population offer a unique opportunity for post-disaster monitoring to help identify psychological support needs. The aim of this study is: 1) to identify...

Interdisciplinary approach to identify language markers for post-traumatic stress disorder using machine learning and deep learning.

Scientific reports
Post-traumatic stress disorder (PTSD) lacks clear biomarkers in clinical practice. Language as a potential diagnostic biomarker for PTSD is investigated in this study. We analyze an original cohort of 148 individuals exposed to the November 13, 2015,...

Deep learning based diagnosis of PTSD using 3D-CNN and resting-state fMRI data.

Psychiatry research. Neuroimaging
BACKGROUND: The incidence rate of Posttraumatic stress disorder (PTSD) is currently increasing due to wars, terrorism, and pandemic disease situations. Therefore, accurate detection of PTSD is crucial for the treatment of the patients, for this purpo...

Use of Machine Learning Algorithms Based on Text, Audio, and Video Data in the Prediction of Anxiety and Posttraumatic Stress in General and Clinical Populations: A Systematic Review.

Biological psychiatry
Research in machine learning (ML) algorithms using natural behavior (i.e., text, audio, and video data) suggests that these techniques could contribute to personalization in psychology and psychiatry. However, a systematic review of the current state...

Enhancing post-traumatic stress disorder patient assessment: leveraging natural language processing for research of domain criteria identification using electronic medical records.

BMC medical informatics and decision making
BACKGROUND: Extracting research of domain criteria (RDoC) from high-risk populations like those with post-traumatic stress disorder (PTSD) is crucial for positive mental health improvements and policy enhancements. The intricacies of collecting, inte...

Speech-based recognition and estimating severity of PTSD using machine learning.

Journal of affective disorders
BACKGROUND: Traditional methodologies for diagnosing post-traumatic stress disorder (PTSD) primarily rely on interviews, incurring considerable costs and lacking objective indices. Integrating biomarkers and machine learning techniques into this diag...

Examining worry and secondary stressors on grief severity using machine learning.

Anxiety, stress, and coping
BACKGROUND & OBJECTIVES: Worry and loss-related secondary stressors appear to be important correlates of problematic grief responses. However, the relative importance of these variables in the context of established correlates of grief responding, ra...

Identifying COVID-19 survivors living with post-traumatic stress disorder through machine learning on Twitter.

Scientific reports
The COVID-19 pandemic has disrupted people's lives and caused significant economic damage around the world, but its impact on people's mental health has not been paid due attention by the research community. According to anecdotal data, the pandemic ...

Paying attention to uncertainty: A stochastic multimodal transformers for post-traumatic stress disorder detection using video.

Computer methods and programs in biomedicine
BACKGROUND AND OBJECTIVES: Post-traumatic stress disorder is a debilitating psychological condition that can manifest following exposure to traumatic events. It affects individuals from diverse backgrounds and is associated with various symptoms, inc...

CALLM: Enhancing Clinical Interview Analysis Through Data Augmentation With Large Language Models.

IEEE journal of biomedical and health informatics
The global prevalence of mental health disorders is increasing, leading to a significant economic burden estimated in trillions of dollars. In automated mental health diagnosis, the scarcity and imbalance of clinical data pose considerable challenges...