INTERPRETABLE ESTIMATION OF SUICIDE RISK AND SEVERITY FROM COMPLETE BLOOD COUNT PARAMETERS WITH EXPLAINABLE ARTIFICIAL INTELLIGENCE METHODS

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dc.authorscopusid12801110400
dc.authorscopusid57211715604
dc.authorscopusid57204843862
dc.authorscopusid58221399900
dc.contributor.authorCansel N.
dc.contributor.authorYagin F.H.
dc.contributor.authorAkan M.
dc.contributor.authorAygul B.I.
dc.date.accessioned2024-08-04T19:59:23Z
dc.date.available2024-08-04T19:59:23Z
dc.date.issued2023
dc.departmentİnönü Üniversitesien_US
dc.description.abstractBackground: 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.en_US
dc.identifier.doi10.24869/psyd.2023.62
dc.identifier.endpage72en_US
dc.identifier.issn0353-5053
dc.identifier.issue1en_US
dc.identifier.pmid37060594en_US
dc.identifier.scopus2-s2.0-85152551847en_US
dc.identifier.scopusqualityQ3en_US
dc.identifier.startpage62en_US
dc.identifier.urihttps://doi.org/10.24869/psyd.2023.62
dc.identifier.urihttps://hdl.handle.net/11616/90602
dc.identifier.volume35en_US
dc.indekslendigikaynakScopusen_US
dc.indekslendigikaynakPubMeden_US
dc.language.isoenen_US
dc.publisherMedicinska Naklada Zagreben_US
dc.relation.ispartofPsychiatria Danubinaen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.subjectCBCen_US
dc.subjectExplainable Artificial Intelligenceen_US
dc.subjectmachine learningen_US
dc.subjectnonviolent suicideen_US
dc.subjectsuicidalityen_US
dc.subjectviolent suicideen_US
dc.titleINTERPRETABLE ESTIMATION OF SUICIDE RISK AND SEVERITY FROM COMPLETE BLOOD COUNT PARAMETERS WITH EXPLAINABLE ARTIFICIAL INTELLIGENCE METHODSen_US
dc.typeArticleen_US

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