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ArticleFrontiers in psychology2026

Delusional themes and forensic status in schizophrenia spectrum disorders: a machine learning based discriminative study.

Amr A N Eleryan, Friederike Boudriot, Marc Dörner, Andreas B Hofmann, Lena Machetanz, Johannes Kirchebner

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Article in Frontiers in psychology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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5 · Who and what money

Authors and funding

6 authors.

Amr A N EleryanUniversity of Zurich, Zürich, Switzerland.
Friederike BoudriotDepartment of Forensic Psychiatry and Psychotherapy, Psychiatrische Dienste Aargau, Windisch, Switzerland.
Marc DörnerDepartment of Consultation-Liaison-Psychiatry and Psychosomatic Medicine, University Hospital Zurich, University of Zurich, Zurich, Switzerland.
Andreas B HofmannAdult Psychiatry and Psychotherapy, University Hospital of Psychiatry Zurich, University of Zurich, Zurich, Switzerland.
Lena MachetanzForensic Psychiatry and Psychotherapy, University Hospital of Psychiatry Zurich, University of Zurich, Zurich, Switzerland.
Johannes KirchebnerForensic Psychiatry and Psychotherapy, University Hospital of Psychiatry Zurich, University of Zurich, Zurich, Switzerland.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: Schizophrenia spectrum disorders (SSD) are associated with an elevated risk of violent and offending behavior; however, the specific contribution of delusional content remains insufficiently understood. Prior research has yielded inconsistent findings regarding the relationship between particular psychopathological symptoms and delusional themes and criminal behavior. A better understanding of symptom-specific phenomenological differences between forensic psychiatric (FPP) and general psychiatric patients (GPP) may inform clinical assessment in forensic settings. This study therefore aimed to examine whether specific psychopathological symptoms discriminate between FPP and GPP with SSD. Methods: In this retrospective study, data from 740 patients with SSD (370 FPP, 370 GPP) treated at a Swiss university hospital were analyzed. From an initial dataset comprising over 500 variables, 103 symptom-focused variables were selected to isolate psychopathological characteristics. Following preprocessing, the dataset was split into training and validation subsets. After the application of dimension reduction techniques, multiple supervised machine learning algorithms were applied and compared using nested cross-validation. Model performance was assessed using area under the curve (AUC), balanced accuracy, sensitivity, and specificity. A Results: Naïve Bayes achieved the best performance (AUC = 0.81, balanced accuracy = 78.2%), with stable results in the validation sample. Two variables emerged as key discriminators: bizarre delusions (46% in FPP vs. 2% in GPP) and aggressive delusional content (69% vs. 19%). Global symptom severity measures, including PANSS scores, did not contribute to the model. The Conclusion: The qualitative content of delusions - particularly bizarre and aggressive themes - shows substantial discriminative value for forensic status in SSD, beyond overall symptom severity and established comorbid risk factors. These findings highlight the relevance of symptom-specific, cognitive-affective mechanisms in forensic evaluations. Incorporating structured assessment of delusional content may complement existing psychometric approaches in forensic psychiatric evaluations.

Indexed as

criminal behaviordelusionsmachine learningpsychopathologyschizophrenia spectrum disorders

Identifiers

PMID42558267
PMCPMC13437467

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