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    Analysis of posttraumatic embitterment disorders by machine learning: Could sullenness be a predictor of posttraumatic embitterment disorder?
    (Kare Publ, 2022) Kilic, Bahar; Karakaplan, Mustafa; Unal, Suheyla
    Objective: This study aimed to determine some fundamental factors specific to posttraumatic embitterment disorder (PTED) using deep machine learning (ML) and network analysis techniques. Method: Sociodemographic data form, Buss-Perry Aggression Questionnaire, Brief Symptom Inventory (BSI), PTED Self-Rating Scale (PTED Scale), and list of stressful life events were administered to 557 people who applied to the outpatient anxiety clinic. ML method and network analysis were applied with the 33 most significant variables. Results: PIED was found in urban areas (p=0.006), individual health problems (p=0.029), early separation from their families (p=0.040), previous trauma (p=0.021), describing childhood sexual abuse (p<0.001), and those with the illness for more than 10 years (p<0.001) were detected at a higher rate than those without. The PIED score was higher in those with an anxiety disorder (p=0.043) and a personality disorder (p<0.001). Almost all life stressors were higher in the PIED group. There was a statistically significant difference between the groups in all subscales of the BSI. When the ML procedure was applied, sullenness was identified as the main symptom of PIED. The factors most associated with sullenness were well-being, hopelessness, and painful event experience. Conclusion: The higher rate of chronic trauma in the group with PTED and the detection of sullenness as the main symptom have been important data for understanding the psychopathological process.
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    Can Temperament and Character Traits Be Used in the Diagnostic Differentiation of Children With ADHD?
    (Lippincott Williams & Wilkins, 2021) Ucuz, Ilknur; Cicek, Ayla Uzun; Cansel, Neslihan; Kilic, Bahar; Colak, Cemil; Yazici, Ipek Percinel; Kilic, Fatma
    In this study, it was aimed to determine the contributions of temperament and character traits to the diagnosis of attention deficit hyperactivity disorder (ADHD) in children. Thirty-six patients between the ages of 9 and 14 with a diagnosis of combined type ADHD and 39 healthy children were included in the study. The Schedule for Affective Disorders and Schizophrenia for School-Age Children-Present and Lifetime Version-Turkish Version and the Turgay DSM-IV Disruptive Behavior Disorders Rating Scale parent form were used to assess hyperactivity/impulsivity and inattentiveness, and comorbid disorders. The Junior Temperament and Character Inventory-Revised form was used to evaluate temperament-character traits. The classification-based association rules (CBARs) method was used for finding rules predicting ADHD accurately. Low persistence and self-directedness scores, and higher disorderliness and fatigability subgroup scores were found in the ADHD group. In CBARs, the separation of children with ADHD from healthy controls could be made with 0.83 accuracy, 0.80 sensitivity, and 0.86 specificity. The results of our study support the view that temperament-character traits can help clinical diagnosis of ADHD.