Qiwei He

Assessing impact of differential symptom functioning on post-traumatic stress disorder (PTSD) diagnosis

This article explores the generalizability of the Diagnostic and Statistical Manual of Mental Disorders, Fourth Edition (DSM‐IV) diagnostic criteria for post‐traumatic stress disorder (PTSD) to various subpopulations. Besides identifying the differential symptom functioning (also referred to as differential item functioning [DIF]) related to various background variables such as gender, marital status and educational level, this study emphasizes the importance of evaluating the impact of DIF on population inferences as made in health surveys and clinical trials, and on the diagnosis of individual patients. Using a sample from the National Comorbidity Study‐Replication (NCS‐R), four symptoms for gender, one symptom for marital status, and three symptoms for educational level were significantly flagged as DIF, but their impact on diagnosis was fairly small. We conclude that the DSM‐IV diagnostic criteria for PTSD do not produce substantially biased results in the investigated subpopulations, and there should be few reservations regarding their use. Further, although the impact of DIF (i.e. the influence of differential symptom functioning on diagnostic results) was found to be quite small in the current study, we recommend that diagnosticians always perform a DIF analysis of various subpopulations using the methodology presented here to ensure the diagnostic criteria is valid in their own studies. Copyright

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.

Selecting testlet features with predictive value for the testlet effect: an empirical study

High-stakes tests often consist of sets of questions (i.e., items) grouped around a common stimulus. Such groupings of items are often called testlets. A basic assumption of item response theory (IRT), the mathematical model commonly used in the analysis of test data, is that individual items are independent of one another. The potential dependency among items within a testlet is often ignored in practice. In this study, a technique called tree-based regression (TBR) was applied to identify key features of stimuli that could properly predict the dependence structure of testlet data for the Analytical Reasoning section of a high-stakes test. Relevant features identified included Percentage of “If” Clauses, Number of Entities, Theme/Topic, and Predicate Propositional Density; the testlet effect was smallest for stimuli that contained 31% or fewer “if” clauses, contained 9.8% or fewer verbs, and had Media or Animals as the main theme. This study illustrates the merits of TBR in the analysis of test data.