Response style and posttraumatic stress disorder (PTSD): a review

Combining Text Mining of Long Constructed Responses and Item-Based Measures: A Hybrid Test Design to Screen for Posttraumatic Stress Disorder (PTSD)

This article introduces a new hybrid intake procedure developed for posttraumatic stress disorder (PTSD) screening, which combines an automated textual assessment of respondents’ self-narratives and item-based measures that are administered consequently. Text mining technique and item response modeling were used to analyze long constructed response (i.e., self-narratives) and responses to standardized questionnaires (i.e., multiple choices), respectively. The whole procedure is combined in a Bayesian framework where the textual assessment functions as prior information for the estimation of the PTSD latent trait. The purpose of this study is twofold: first, to investigate whether the combination model of textual analysis and item-based scaling could enhance the classification accuracy of PTSD, and second, to examine whether the standard error of estimates could be reduced through the use of the narrative as a sort of routing test. With the sample at hand, the combination model resulted in a reduction in the misclassification rate, as well as a decrease of standard error of latent trait estimation. These findings highlight the benefits of combining textual assessment and item-based measures in a psychiatric screening process. We conclude that the hybrid test design is a promising approach to increase test efficiency and is expected to be applicable in a broader scope of educational and psychological measurement in the future.

Association between posttraumatic stress disorder (PTSD) severity and ego structure of the Nanai people

BACKGROUND

A man-made chemical disaster occurred in the Amur River, leading to posttraumatic stress disorder (PTSD) in the Nanai people indigenous to the river's surrounding area. PTSD severity measured by the total scores of Impact of Event Scale-Revised (IES-R) (Total-I) and Clinician-Administered PTSD Scale (CAPS) (Total-C) were not always identical in terms of demographic and ethnocultural characters. It is possible that the results derived using the Total-I and Total-C may differ for persons with different backgrounds and/or individual characteristics. In this study, the associations between PTSD severity and personal characteristics were evaluated.

METHODS

The study was a field-type survey including 187 randomly selected participants (75 males and 112 females). In addition to Total-I/Total-C, scores for each IES-R/CAPS item, Intrusion, Avoidance, and Hyperarousal, and Ego Structure Test by Ammon (ISTA) score were examined to evaluate their personal characteristics.

RESULTS

No specific trends in ISTA score were obvious among four groups defined according to Total-I/Total-C. The results of principal component analysis showed that all IES-R/CAPS items contributed positively to the 1st axis but to the 2nd axis in a different manner. ISTA items did not always show correlations to each other, but principal component analysis suggested that Construct contributed positively and Destruct and Deficient (with the exception of Destruct sexuality) contributed negatively. High IES-R scores were associated with Construct Aggression and Deficient Inner demarcation, but high CAPS score was less likely to exhibit Construct Narcissism.

CONCLUSION

To avoid the misdiagnosis of PTSD, usage of both IES-R/CAPS may be required. Simultaneous application of personality/ego tests may be helpful, but appropriate numbers of their questions would be important.

Automated Assessment of Patients' Self-Narratives for Posttraumatic Stress Disorder Screening Using Natural Language Processing and Text Mining

Patients' narratives about traumatic experiences and symptoms are useful in clinical screening and diagnostic procedures. In this study, we presented an automated assessment system to screen patients for posttraumatic stress disorder via a natural language processing and text-mining approach. Four machine-learning algorithms-including decision tree, naive Bayes, support vector machine, and an alternative classification approach called the product score model-were used in combination with n-gram representation models to identify patterns between verbal features in self-narratives and psychiatric diagnoses. With our sample, the product score model with unigrams attained the highest prediction accuracy when compared with practitioners' diagnoses. The addition of multigrams contributed most to balancing the metrics of sensitivity and specificity. This article also demonstrates that text mining is a promising approach for analyzing patients' self-expression behavior, thus helping clinicians identify potential patients from an early stage.

Screening for posttraumatic stress disorder using verbal features in self narratives: a text mining approach

Much evidence has shown that people's physical and mental health can be predicted by the words they use. However, such verbal information is seldom used in the screening and diagnosis process probably because the procedure to handle these words is rather difficult with traditional quantitative methods. The first challenge would be to extract robust information from diversified expression patterns, the second to transform unstructured text into a structuralized dataset. The present study developed a new textual assessment method to screen the posttraumatic stress disorder (PTSD) patients using lexical features in the self narratives with text mining techniques. Using 300 self narratives collected online, we extracted highly discriminative keywords with the Chi-square algorithm and constructed a textual assessment model to classify individuals with the presence or absence of PTSD. This resulted in a high agreement between computer and psychiatrists' diagnoses for PTSD and revealed some expressive characteristics in the writings of PTSD patients. Although the results of text analysis are not completely analogous to the results of structured interviews in PTSD diagnosis, the application of text mining is a promising addition to assessing PTSD in clinical and research settings.

Measuring symptom exaggeration in veterans with chronic posttraumatic stress disorder

Veteran subjects with chronic, combat-related posttraumatic stress disorder (PTSD) are frequently used as research subjects in the study of PTSD. However, questions have consistently been raised regarding PTSD symptom exaggeration in veteran populations due to the relationship between PTSD symptoms and disability payments within the Veterans Affairs (VA) system. We used a variety of standardized forensic instruments frequently utilized in measuring symptom exaggeration - including the MMPI-2, the Structured Interview for Reported Symptoms (SIRS), the Structured Inventory of Malingered Symptomatology (SIMS), and the Miller Forensic Assessment Test (MFAST) - to examine symptom report in a group of veterans presenting for treatment at a VA residential PTSD treatment program. The majority of Vietnam veteran subjects in our study (53%) exhibited clear symptom exaggeration by SIRS criteria. Within the entire subject group, total SIRS scores correlated significantly with reported PTSD symptom severity as measured by the Clinician Administered PTSD Scale (CAPS).

Detection of malingered PTSD: an overview of clinical, psychometric, and physiological assessment: where do we stand?

Posttraumatic stress disorder (PTSD) is a condition that can be easily malingered for secondary gain. For this reason, it is important for physicians to understand the phenomenology of true PTSD and indicators that suggest an individual is malingering. This paper reviews the prevalence of PTSD for both the general population and for specific events, such as rape and terrorism, to familiarize evaluators with the frequency of its occurrence. The diagnostic criteria for PTSD, as well as potential ambiguities in the criteria, such as what constitutes an exposure to a traumatic event, are reviewed. Identified risk factors are reviewed as a potential way to help differentiate true cases of PTSD from malingered cases. The question of symptom overreporting as a feature of the disease versus a sign of malingering is discussed. We then examine how the clinician can use the clinical interview (e.g., SIRS, CAPS), psychometric testing, and the patient's physiological responses to detect malingering. Particular attention is paid to research on the MMPI and the subscales of infrequency (F), infrequency-psychopathology (Fp), and infrequency-posttraumatic stress disorder (Fptsd). Research and questions regarding the accuracy of self-report questionnaires, specifically the Mississippi Scale (MSS) and the Personality Assessment Inventory (PAI), are examined. Validity, usability, and cutoff values for other psychometric tests, checklists, and physiological tests are discussed. The review includes a case, which shows how an individual used symptom checklist information to malinger PTSD and the inconsistencies in his story that the evaluator detected. We conclude with a discussion regarding future diagnostic criteria and suggestions for research, including a systematic multifaceted approach to identify malingering.