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    INTERPRETABLE ESTIMATION OF SUICIDE RISK AND SEVERITY FROM COMPLETE BLOOD COUNT PARAMETERS WITH EXPLAINABLE ARTIFICIAL INTELLIGENCE METHODS
    (Medicinska Naklada Zagreb, 2023) Cansel N.; Yagin F.H.; Akan M.; Aygul B.I.
    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. © Medicinska naklada, Zagreb & School of Medicine, University of Zagreb & Pro mente, Zagreb, Croatia.
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    A NEW ARTIFICIAL INTELLIGENCE-BASED CLINICAL DECISION SUPPORT SYSTEM FOR DIAGNOSIS OF MAJOR PSYCHIATRIC DISEASES BASED ON VOICE ANALYSIS
    (Medicinska Naklada Zagreb, 2023) Cansel N.; Alcin Ö.F.; Yılmaz Ö.F.; Ari A.; Akan M.; Ucuz İ.
    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 coe?cients using Wavelet Packet Transform (WPT). Feature extraction was performed using each coe?cient obtained from both Mel-Frequency Cepstral Coe?cients (MFCC) and Gammatone Cepstral Coe?cient (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. © Medicinska naklada – Zagreb, Croatia.