Emotional Dysregulation and Temperament-Character Traits in Adolescents With Functional Neurological Symptom Disorder (Conversion Disorder).

ABSTRACT: The purpose of this study was to evaluate emotion dysregulation and temperament-character traits in adolescents with functional neurological symptom disorder (FNSD). Forty adolescents with FNSD and 40 healthy adolescents were evaluated by a semiconstructed diagnosis interview, Temperament and Character Inventory (TCI), Difficulties in Emotion Regulation Scale (DERS), Regulation of Emotions Questionnaire (REQ), and Children's Somatization Inventory-24 (CSI-24). The external and internal dysfunctional emotion regulation scores of REQ, all subscales of DERS, except the awareness subscale, and CSI-24 scores were significantly higher in FNSD patients compared with healthy controls. There were significant differences between the groups in terms of harm avoidance and reward dependence subscale scores of TCI. Multiple logistic regression analysis showed that the external dysfunctional emotion regulation strategy, somatization, and reward dependence are significant predictors of FNSD. Our results provide evidence that adolescents with FNSD experience emotional dysregulation and that the differential value of some temperament-character traits in the diagnosis of FNSD.

Interpretable Estimation of Suicide Risk and Severity from Complete Blood Count Parameters with Explainable Artificial Intelligence Methods.

BACKGROUND: The peripheral inflammatory markers are important in the pathophysiology of suicidal behavior. However, methods for practical uses haven't been developed enough yet. This study developed predictive models based on explainable artificial intelligence (xAI) that use the relationship between complete blood count (CBC) values and suicide risk and severity of suicide attempt. SUBJECTS AND METHODS: 544 patients who attempted an incomplete suicide between 2010-2020 and 458 healthy individuals were selected. The data were obtained from the electronic registration systems. To develop prediction models using CBC values, the data were grouped in two different ways as suicidal/healthy and attempted/non-attempted violent suicide. The data sets were balanced for the reliability of the results of the machine learning (ML) models. Then, the data was divided into two; 80% of as the training set and 20% as the test set. For suicide prediction, models were created with Random Forest, Logistic Regression, Support vector machines and XGBoost algorithms. SHAP, was used to explain the optimal model. RESULTS: Of the four ML methods applied to CBC data, the best-performing model for predicting both suicide risk and suicide severity was the XGBoost model. This model predicted suicidal behavior with an accuracy of 0.83 (0.78-0.88) and the severity of suicide attempt with an accuracy of 0.943 (0.91-0.976). Lower levels of NEU, WBC, MO, NLR, MLR and, age higher levels of HCT, PLR, PLT, HGB, RBC, EO, MPV and, BA contributed positively to the predictive created model for suicide risk, while lower PLT, BA, PLR and RBC levels and higher MO, EO, HCT, LY, MLR, NEU, NLR, WBC, HGB and, age levels have a positive contribution to the predictive created model for violent suicide attempt. CONCLUSION: Our study suggests that the xAI model developed using CBC values may be useful in detecting the risk and severity of suicide in the clinic.

A NEW ARTIFICIAL INTELLIGENCE-BASED CLINICAL DECISION SUPPORT SYSTEM FOR DIAGNOSIS OF MAJOR PSYCHIATRIC DISEASES BASED ON VOICE ANALYSIS.

BACKGROUND: Speech features are essential components of psychiatric examinations, serving as important markers in the recognition and monitoring of mental illnesses. This study aims to develop a new clinical decision support system based on artificial intelligence, utilizing speech signals to distinguish between bipolar, depressive, anxiety and schizophrenia spectrum disorders. SUBJECTS AND METHODS: A total of 79 patients, who were admitted to the psychiatry clinic between 2020-2021, including 15 with schizophrenia spectrum disorders, 24 with anxiety disorders, 25 with depressive disorders, and 15 with bipolar affective disorder, alongside with 25 healthy individuals were included in the study. The speech signal dataset was created by recording participants' readings of two texts determined by the Russell emotion model. The number of speech samples was increased by using random sampling in speech signals. The sample audio signals were decomposed into time-frequency coefficients using Wavelet Packet Transform (WPT). Feature extraction was performed using each coefficient obtained from both Mel-Frequency Cepstral Coefficients (MFCC) and Gammatone Cepstral Coefficient (GTCC) methods. The disorder classification was carried out using k-Nearest Neighbor (kNN) and Support Vector Machine (SVM) classifiers. RESULTS: The success rate of the developed model in distinguishing the disorders was 96.943%. While the kNN model exhibited the highest performance in diagnosing bipolar disorder, it performed the least effectively in detecting depressive disorders. Whereas, the SVM model demonstrated close and high performance in detecting anxiety and psychosis, but its performance was low in identifying bipolar disorder. The findings support the utilization of speech analysis for distinguishing major psychiatric disorders. In this regard, the future development of artificial intelligence-based systems has the potential to enhance the psychiatric diagnosis process.

Awarding additional MELD points to the shortest waitlist candidates improves sex disparity in access to liver transplant in the United States.

Since the introduction of the MELD-based allocation system, women are now 30% less likely than men to undergo liver transplant (LT) and have 20% higher waitlist mortality. These disparities are in large part due to height differences in men and women though no national policies have been implemented to reduce sex disparities. Patients were identified using the Scientific Registry of Transplant Recipients (SRTR) from 2014 to 2019. Patients were categorized into five groups by first dividing into thirds by height then dividing the shortest third into three groups to capture more granular differences in the most disadvantaged patients (<166 cm). We then used LSAM to model waitlist outcomes in five versions of awarding additional MELD points to shorter candidates compared to current policy. We identified two proposed policy changes LSAM scenarios that resulted in improvement in LT and death percentage for the shortest candidates with the least negative impact on taller candidates. In conclusion, awarding an additional 1-2 MELD points to the shortest 8% of LT candidates would improve waitlist outcomes for women. This strategy should be considered in national policy allocation to address sex-based disparities in LT.

An application of optimal control in medical systems: optimal investment strategy in doctors.

Health care is ever more important with the aging population and with the increased awareness of the importance of the medical systems due to the corona crisis that showed the capacity of the health care infrastructure, especially in terms of numbers of health care personnel such as doctors, was not sufficient. Assuming that the number of doctors per patient is one of the determinants of patient satisfaction, optimal investments in new doctors, specialist doctors and foreign doctors are analyzed. Optimal Control Theory is employed to determine the optimal investment strategy for new doctors (new graduates), specialists and foreign doctors to maximize the net (of costs) patient satisfaction over a fixed time horizon. It is found that a nation with an insufficient number of total doctors and specialist doctors at the beginning of the planning horizon should increase the investment in new doctors as a quadratic function of time, increase the local specialist doctors linearly, while employing foreign doctors as to equate their cost to the marginal satisfaction of patients.

The Relationship Between Suicide Attempt and Gonadotropins, Gonadal Hormones, and Cortisol in Females.

Objective: The aim of this study was to examine the relationship between suicidal behavior and gonadotropins, gonadal hormones, and cortisol in females. Methods: The study included 3 groups of 23 females each, aged 18-45 years; one group comprising those who had attempted suicide, another group of females matched for age, menstrual phase, and body mass index, with depression but no suicidal tendencies, and a control group of 23 healthy females. For all participants, a sociodemographic information form was completed, and the Beck Depression Inventory, the Beck Anxiety Inventory, and the Beck Hopelessness Scale were used. Blood samples were taken at 8 am (in the attempted-suicide group, within 24 hours of the attempt), and follicle-stimulating hormone, luteinizing hormone, estradiol, testosterone, progesterone, and cortisol levels were measured. Results: No statistically significant differences were observed between the groups with respect to gonadotropin and gonadal hormone levels. There were statistically significant differences in the cortisol levels between the attempted suicide and control groups and between the depression and control groups (P < .05). The cortisol levels negatively correlated with all scale scores. Conclusion: Studies on suicidal patients should pay more attention to the potential role of hypocortisolism. More studies with larger samples are needed to investigate the relationship between gonadotropins, gonadal hormones, and suicidal behavior.