Detection Of Posttraumatic Stress Disorder Research Articles

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

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 has raised serious concerns related to mental health among the masses. However, no systematic investigations have been conducted previously on mental health monitoring and, in particular, detection of post-traumatic stress disorder (PTSD). The goal of this study is to use classical machine learning approaches to classify tweets into COVID-PTSD positive or negative categories. To this end, we employed various Machine Learning (ML) classifiers, to segregate the psychotic difficulties with the user’s PTSD in the context of COVID-19, including Random Forest Support Vector Machine, Naïve Bayes, and K-Nearest Neighbor. ML models are trained and tested using various combinations of feature selection strategies to get the best possible combination. Based on our experimentation on real-world dataset, we demonstrate our model’s effectiveness to perform classification with an accuracy of 83.29% using Support Vector Machine as classifier and unigram as a feature pattern.

Archimedes optimization-based Elman Recurrent Neural Network for detection of post-traumatic stress disorder

Post-traumatic stress disorder (PTSD) is a stress-based disorder that occurs when a person is vulnerable to undesirable traumatic events like injury or harm. Numerous research works reported dissimilar complex issues for detecting PTSD severity in patients which makes the PTSD diagnosis process a complicated task. So, a novel Multi-Strategy Seeker Archimedes Optimization-based Elman Recurrent Neural Network (MSSAO-ERNN) technique to detect PTSD symptoms in earlier stages using speech samples. Our proposed MSSAO-ERNN technique efficiently identifies PTSD symptoms from the dataset and helps the physician to diagnose accurately. The proposed approach constitutes various significant phases that include the pre-processing phase, feature extraction phase, and classification phase to detect the presence of PTSD in the datasets. The performance of the proposed method is determined by computing various metrics for the accurate identification of PTSD. Finally, three different datasets namely the NNE dataset, the FME hospital dataset as well as TIMIT dataset are employed in this paper for PTSD diagnosis. The experimental results revealed that the proposed method attained a superior accuracy of 97% under different PTSD techniques.

Machine learning for post-traumatic stress disorder identification utilizing resting-state functional magnetic resonance imaging.

Early detection of post-traumatic stress disorder (PTSD) is essential for proper treatment of the patients to recover from this disorder. The aligned purpose of this study was to investigate the performance deviations in regions of interest (ROI) of PTSD than the healthy brain regions, to assess interregional functional connectivity and applications of machine learning techniques to identify PTSD and healthy control using resting-state functional magnetic resonance imaging (rs-fMRI). The rs-fMRI data of 10 ROI was extracted from 14 approved PTSD subjects and 14 healthy controls. The rs-fMRI data of the selected ROI were used in ANOVA to measure performance level and Pearson's correlation to investigate the interregional functional connectivity in PTSD brains. In machine learning approaches, the logistic regression, K-nearest neighbor (KNN), support vector machine (SVM) with linear, radial basis function, and polynomial kernels were used to classify the PTSD and control subjects. The performance level in brain regions of PTSD deviated as compared to the regions in the healthy brain. In addition, significant positive or negative functional connectivity was observed among ROI in PTSD brains. The rs-fMRI data have been distributed in training, validation, and testing group for maturity, implementation of machine learning techniques. The KNN and SVM with radial basis function kernel were outperformed for classification among other methods with high accuracies (96.6%, 94.8%, 98.5%) and (93.7%, 95.2%, 99.2%) to train, validate, and test datasets, respectively. The study's findings may provide a guideline to observe performance and functional connectivity of the brain regions in PTSD and to discriminate PTSD subject using only the suggested algorithms.

Role of vascular function in early detection of posttraumatic stress disorder, and monitoring response to trauma-informed interventions

Distant soft-tissue metastases (subcutaneous tissues and skeletal muscle) are extremely rare, specially in esophageal carcinoma. We describe a patient who was treated for oesophageal adenocarcinoma 2.5 years before. A PET/CT was performed showing metastatic spread because of a solitary focus of increased tracer uptake corresponding one subcutaneous node in the upper abdomen. An excisional biopsy proved to be a metastasis from the carcinoma. Restaging 18F-FDG PET/CT study was performed 2 years later demonstrating some focus of increased uptake within several muscles as isolated distant hematogenous spread of metastases, as they were histopathologically confirmed.As most of soft-tissue metastases are asymptomatic, the physicians should recommend histopathological study of focus of FDG uptake at subcutaneous tissues and/or skeletal muscles, because they may be the first sign of disease expansion so therapeutic management of these patients could be changed.Las metástasis en partes blandas (tejido celular subcutáneo y músculo esquelético) son extremadamente raras, especialmente en cáncer de esófago. Presentamos un varón con antecedente de adenocarcinoma de esófago tratado 2,5 años antes, con hallazgo en 18F-FDG PET/TC de afectación metastásica a distancia exclusiva en un nódulo subcutáneo en pared abdominal, con confirmación anatomopatológica tras su resección. En nuevo PET-TAC 18F-FDG realizado 2 años después se objetivó nueva afectación a distancia pero exclusiva en musculatura esquelética, también confirmada histológicamente.Como las lesiones en partes blandas frecuentemente son asintomáticas, ante el hallazgo en un estudio PET de lesiones subcutáneas y/o musculares hipermetabólicas debemos recomendar su estudio histológico, ya que pueden tratarse del primer signo de una metastatización a distancia cambiando radicalmente el manejo terapéutico del paciente.

Psychometric properties of the Post Traumatic Stress Disorder Checklist for DSM-5 (PCL-5) in Greek women after cesarean section.

The aim of this study was to examine psychometric properties of the revised Posttraumatic Stress Checklist (PCL-5) for Diagnostic and Statistical Manual- 5th Edition (DSM-5) in Greek postpartum women after Cesarean Section(CS) (emergency-elective).So far, there was no study in Greece assessing psychometric properties of the PCL-5 in women after CS. The participating women (N = 469), who gave birth with emergency and elective CS at the Greek University Hospital of Larisa, have consented to participate in two phases of the survey and completed self-report questionnaires, the 2nd day after CS and at the 6th week after CS. Measures used in this study were the PCL-5 for DSM-5, the Life Events Checklist (LEC-5), Criteria B, C, D, E, and Criterion A, specifically designed for detection of posttraumatic stress disorder (PTSD) symptoms in postpartum period. To evaluate the internal reliability of the PCL-5 two different indices of internal consistency were calculated, i.e., Cronbach's alpha (.97) and Guttman'ssplit-half (.95), demonstrating high reliability level. The data were positively skewed, suggesting that the reported levels of PTSD among our participants were low. Factor analyses demonstrated acceptable construct validity; a comparison of thePCL-5 with the other measures of the same concept showed a good convergent validity of the scale. Overall, all the results suggest that the four-factor PCL-5 seemed to work adequately for the Greek sample of women after CS.

The sound of silence: Breathing analysis for finding traces of trauma and depression in oral history archives

Abstract Many people experience a traumatic event during their lifetime. In some extraordinary situations, such as natural disasters, war, massacres, terrorism, or mass migration, the traumatic event is shared by a community and the effects go beyond those directly affected. Today, thanks to recorded interviews and testimonials, many archives and collections exist that are open to researchers of trauma studies, holocaust studies, and historians, among others. These archives act as vital testimonials for oral history, politics, and human rights. As such, they are usually either transcribed or meticulously indexed. In this work, we propose to look at the nonverbal signals emitted by victims of various traumatic events when they describe the trauma and we seek to render these for novel representations without taking into account the explicit verbal content. Our preliminary paralinguistic analysis on a manually annotated collection of testimonials from different archives, as well as on a corpus prepared for depression and post-traumatic stress disorder detection indicates a tentative connection between breathing and emotional states of speakers, which opens up new possibilities of exploring oral history archives.

Continuous monitoring and detection of post-traumatic stress disorder (PTSD) triggers among veterans: A supervised machine learning approach

Post-traumatic stress disorder (PTSD) is a prevalent mental health condition among United States combat veterans, associated with high incidence of suicide and substance abuse. While PTSD treatments exist, such methods are limited to in-person therapy sessions and medications. Tools and technologies to monitor patients continuously, especially between sessions, are largely absent. This article documents efforts to develop predictive algorithms that utilize real-time heart rate data, collected using commercial off-the-shelf wearable sensors, to detect the onset of PTSD triggers. The heart rate data, pre-processed with a Kalman filter imputation approach to resolve missing data, were used to train five algorithms: decision tree, support vector machine, random forest, neural network, and convolutional neural network. Prediction performance was assessed with the Area Under the receiver operating characteristic Curve (AUC). The convolutional neural network, support vector machine, and random forests had the highest AUC and significantly outperformed a random classifier. Further analysis of the heart rate data and predictions suggest that the algorithms associate an increase in heart rate with PTSD trigger onset. While work is needed to enhance algorithm performance and robustness, these results suggest that wearable monitoring technology for PTSD symptom mitigation is an achievable goal in the near future.

Randomized controlled trial of multidisciplinary team stress and performance in immersive simulation for management of infant in shock: study protocol

BackgroundHuman error and system failures continue to play a substantial role in adverse outcomes in healthcare. Simulation improves management of patients in critical condition, especially if it is undertaken by a multidisciplinary team. It covers technical skills (technical and therapeutic procedures) and non-technical skills, known as Crisis Resource Management. The relationship between stress and performance is theoretically described by the Yerkes-Dodson law as an inverted U-shaped curve. Performance is very low for a low level of stress and increases with an increased level of stress, up to a point, after which performance decreases and becomes severely impaired. The objectives of this randomized trial are to study the effect of stress on performance and the effect of repeated simulation sessions on performance and stress.MethodsThis study is a single-center, investigator-initiated randomized controlled trial including 48 participants distributed in 12 multidisciplinary teams. Each team is made up of 4 persons: an emergency physician, a resident, a nurse, and an ambulance driver who usually constitute a French Emergency Medical Service team. Six multidisciplinary teams are planning to undergo 9 simulation sessions over 1 year (experimental group), and 6 multidisciplinary teams are planning to undergo 3 simulation sessions over 1 year (control group). Evidence of the existence of stress will be assessed according to 3 criteria: biological, electrophysiological, and psychological stress. The impact of stress on overall team performance, technical procedure and teamwork will be evaluated. Participant self-assessment of the perceived impact of simulations on clinical practice will be collected. Detection of post-traumatic stress disorder will be performed by self-assessment questionnaire on the 7th day and after 1 month.DiscussionWe will concomitantly evaluate technical and non-technical performance, and the impact of stress on both. This is the first randomized trial studying repetition of simulation sessions and its impact on both clinical performance and stress, which is explored by objective and subjective assessments. We expect that stress decreases team performance and that repeated simulation will increase it. We expect no variation of stress parameters regardless of the level of performance.Trial registrationClinicalTrials.gov registration number NCT02424890

Patient factors relating to detection of posttraumatic stress disorder in Department of Veterans Affairs primary care settings

We examined the impact of patient-level factors on provider recognition of posttraumatic stress disorder (PTSD). Analyses were based on a random sample of 1,079 consenting patients who had an outpatient visit at any of four southeastern Department of Veterans Affairs hospitals in 1999. We collected data on PTSD symptoms, sociodemographics, functional status, medical record diagnoses, and independent PTSD diagnostic assessments for 888 patients. Complete and usable data were available for 819 patients. A total of 98 patients (12%) met criteria for PTSD, and of these, 42 (43%) were correctly classified as such by their provider. Results indicate that age (50-64), war-zone service, worse functioning on the 36-Item Short Form Health Survey role emotional subscale, a diagnosis of musculoskeletal pain, a greater percentage of persistent reexperiencing or avoidance/numbing symptoms, and a previously diagnosed substance use disorder were all independently related to provider recognition of PTSD. Knowledge of these factors may help inform providers and direct improved screening and case finding.

Early Detection of Posttraumatic Stress Disorder in Children

Surviving trauma patients are at an increased risk for developing posttraumatic stress disorder (PTSD) symptoms. An estimated 1 million American children develop some form (acute, chronic) of PTSD every year. The purpose of this study was to benchmark detection of PTSD in children at our trauma center against the national average and develop a guideline that would identify children who might need referral for screening prior to discharge. A collaborative effort among the trauma, rehabilitation, neuropsychology, and pediatric personnel resulted in a guideline to address early detection of PTSD in children. After implementation of the guideline, there was a 40% increase in detection of PTSD symptoms in identified children who were screened. We conclude that a systematic identification of PTSD triggers increases the detection rate of PTSD and opportunities for screening and intervention.

Establishment of a disaster-related psychological screening test.

To create a short screening scale for the detection of posttraumatic stress disorder (PTSD) and major depressive disorder (MDE) in earthquake survivors in Taiwan. Trained research assistants used the Disaster-Related Psychological Screening Test (DRPST) to assess 461 residents of a village that had experienced a major earthquake. The participants were also evaluated by psychiatrists using the Mini-International Neuro-psychiatric Interview (MINI). Best subset regression analysis and the receiver operating characteristics curve were used to select a subset of items and cut-off points from the DRPST. A seven-symptom scale and a three-symptom analogue were selected for PTSD and MDE screening, respectively. Scores of three or more on the PTSD scale and two or more on the MDE scale were used to define a group of positive cases that provide useful information for the patient cohort and will be valuable in long-term follow-up studies of the prevalence of psychiatric diseases following a natural disaster. However, higher scores could also be used to define positive cases under limited psychiatric care resources. The DRPST, which was administered for phase 1 of this two-phase study, may be used for effective and rapid screening for PTSD and MDE after an earthquake, despite the usual limitations on resources following a disaster.

CLINICAL NOTE ON MMPI POSTTRAUMATIC STRESS DISORDER SCALE (PK)

An MMPI scale for the detection of Posttraumatic Stress Disorder has been altered in MMPI-2. The entire scale, not readily retrievable, is presented for the MMPI group form.

Clinical Note on MMPI Posttraumatic Stress Disorder Scale (PK)

An MMPI scale for the detection of Posttraumatic Stress Disorder has been altered in MMPI-2. The entire scale, not readily retrievable, is presented for the MMPI group form.