A megakadásjelenségek és a temporális paraméterek szerepe a borderline személyiségzavar felismerésében
Borderline personality disorder (BPD) is characterized by a pervasive pattern of instability of identity, emotions, and interpersonal relationships, and difficulty with emotional and impulse control. Due to the complex system of diagnostic criteria and frequent comorbid disorders, borderline population is relatively heterogeneous, making it difficult for psychiatrists to diagnose individuals. As speech is a form of behavior, it is one of the objects of psychiatric examination, and its characteristics can be considered symptoms. Our goal is to differentiate borderline individuals (N = 27) from healthy controls (N = 27) based on the patterns of disfluencies and temporal parameters of spontaneous speech. We have built a classification model that predicts the likelihood of BPD in an individual with 0.834 AUROC based on the frequency of silent pauses, filled pauses, and disturbances of grammatical encoding (grammatical errors and blendings).