[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-126520-en":3,"doc-seo-126520-105":31,"detail-sidebar-cat-0-en-105":92},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":20,"is_deleted":4,"is_public":21,"is_downloadable":21,"audit_status":21,"page_count":22,"language":23,"language_code":24,"site_id":25,"html_lang":24,"table_of_contents":26,"faqs":27,"seo_title":28,"seo_description":14,"update_tm":29,"read_time":30},126520,962085662650,"Jiven","https://ap-avatar.wpscdn.com/davatar_29158cc5080c5b710cf443261637dec0",8,"Research & Report","Exploring Characteristics of Homicide Offenders With Schizophrenia Spectrum Disorders Via Machine Learning","The study examines factors linked to completed homicides committed by offenders diagnosed with schizophrenia who were referred to a Swiss forensic institution. Machine learning models were applied to data from 370 inpatients at the Centre for Inpatient Forensic Therapy at the Zurich University Hospital of Psychiatry. From 519 explored variables, nested resampling with variable filtering, model building, and embedded selection identified ten influential criminal, psychiatric history, and clinical factors distinguishing homicidal from other offenders. Results broaden evidence on completed homicide risk in schizophrenia, with limitations, clinical relevance, and future directions addressed.","Zurich Open Repository and Archive  \nUniversity of Zurich  \nUniversity Library Strickhofstrasse 39  \nCH-8057 Zurich [www.zora.uzh.ch](www.zora.uzh.ch)  \nYear: 2022  \nExploring Characteristics of Homicide Offenders With Schizophrenia Spectrum  \nDisorders Via Machine Learning  \nSonnweber, Martina ; Lau, Steffen ; Kirchebner, Johannes  \nAbstract: The link between schizophrenia and homicide has long been the subject of research with significant impact on mental health policy, clinical practice, and public perception of people with psychiatric disorders. The present study investigates factors contributing to completed homicides committed by offenders diagnosed with schizophrenia referred to a Swiss forensic institution, using machine learning algorithms. Data were collected from 370 inpatients at the Centre for Inpatient Forensic Therapy at the Zurich University Hospital of Psychiatry. A total of 519 variables were explored to differentiate homicidal and other (violent and non-violent) offenders. The dataset was split employing variable filtering, model building, and selection embedded in a nested resampling approach. Ten factors regarding criminal and psychiatric history and clinical factors were identified to be influential in differentiating between homicidal and other offenders. Findings expand the research on influential factors for completed homicide in patients with schizophrenia. Limitations, clinical relevance, and future directions are discussed.  \nDOI: [https://doi.org/10.1177/0306624x221102799](https://doi.org/10.1177/0306624x221102799)  \nPosted at the Zurich Open Repository and Archive, University of Zurich ZORA URL: [https://doi.org/10.5167/uzh-224900](https://doi.org/10.5167/uzh-224900)  \nJournal Article Published Version  \nThe following work is licensed under a Creative Commons: Attribution-NonCommercial 4.0 International (CC BY-NC 4 .0) License.  \nOriginally published at:  \nSonnweber, Martina; Lau, Steffen; Kirchebner, Johannes (2022) . Exploring Characteristics of Homicide Offenders With Schizophrenia Spectrum Disorders Via Machine Learning. International Journal of Offender Therapy and Comparative Criminology:Epub ahead of print.  \nDOI: [https://doi.org/10.1177/0306624x221102799](https://doi.org/10.1177/0306624x221102799)  \nArticle  \nExploring Characteristics of Homicide Offenders With Schizophrenia Spectrum Disorders Via Machine Learning  \nMartina Sonnweber 1, Steffen Lau 1,  \nand Johannes Kirchebner 1  \nInternational Journal of Offender Therapy and Comparative Criminology 1–20  \n© The Author(s) 2022  \nArticle reuse guidelines:  \n[sagepub.com/journals-permissions](sagepub.com/journals-permissions)[ ](sagepub.com/journals-permissions)[DOI: 10.1177/0306624X221102799](DOI: 10.1177/0306624X221102799)[ ](DOI: 10.1177/0306624X221102799)[journals.sagepub.com/home/ijo](journals.sagepub.com/home/ijo)  \nAbstract  \nThe link between schizophrenia and homicide has long been the subject of research with significant impact on mental health policy, clinical practice, and public perception of people with psychiatric disorders. The present study investigates factors contributing to completed homicides committed by offenders diagnosed with schizophrenia referred to a Swiss forensic institution, using machine learning algorithms. Data were collected from 370 inpatients at the Centre for Inpatient Forensic Therapy at the Zurich University Hospital of Psychiatry. A total of 519 variables were explored to differentiate homicidal and other (violent and non-violent) offenders. The dataset was split employing variable filtering, model building, and selection embedded in a nested resampling approach. Ten factors regarding criminal and psychiatric history and clinical factors were identified to be influential in differentiating between homicidal and other offenders. Findings expand the research on influential factors for completed homicide in patients with schizophrenia. Limitations, clinical relevance, and future directions are discussed.","cbCaidMOqWVvvDjV","https://ap.wps.com/l/cbCaidMOqWVvvDjV","pdf",446865,2,1,21,"English","en",105,"# Abstract\n# Background and Rationale\n## Schizophrenia Spectrum Disorders and Violence Risk\n## Incidence and Comparative Risk\n## Victim Relationship Patterns","[{\"question\":\"What is the main goal of this study?\",\"answer\":\"To investigate factors contributing to completed homicides committed by offenders diagnosed with schizophrenia spectrum disorders using machine learning.\"},{\"question\":\"How was the dataset constructed and analyzed?\",\"answer\":\"Data came from 370 inpatients at a Swiss forensic therapy center, with 519 variables analyzed using nested resampling, variable filtering, model building, and embedded model selection.\"},{\"question\":\"Which factors were identified as most influential?\",\"answer\":\"Ten factors related to criminal and psychiatric history plus clinical factors were found to be influential in distinguishing homicidal from other offenders.\"}]","Exploring Characteristics of Homicide Offenders With Schizophrenia Spectrum Disorders Via Machine Learning | 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