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

Journal: Psychiatria Danubina

PMID: 37060594

Abstract

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.

Authors

Neslihan Cansel

Inonu University Faculty of Medicine, Department of Psychiatry, Malatya, Turkey.
Fatma Hilal Yagin

Department of Biostatistics and Medical Informatics, Faculty of Medicine, Inonu University, Malatya, Türkiye.
Mustafa Akan
Bedriye Ilkay Aygul

Keywords

Adolescent Adult Aged Aged, 80 and over Artificial Intelligence Blood Cell Count Case-Control Studies Female Humans Male Middle Aged Models, Statistical Reproducibility of Results Risk Assessment Suicidal Ideation Suicide Suicide, Attempted Young Adult

External Resources

View on PubMed Access via DOI PubMed (37060594)

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

Abstract

Authors

Keywords

External Resources

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