The rapid increase in data sample sizes in psychiatry and psychology makes modern statistical analysis methods increasingly crucial in these fields. Machine learning (ML) offers the possibility of identifying complex relationships within large and complex data sets and supporting scientific work. Also, within the specific subfield of forensic psychiatry, the abundance of such datasets is increasing rapidly. In the meanwhile, publications performing complex analyses via ML can be observed, especially in forensic-psychiatric risk prediction. Although this offers exciting opportunities, the links between aggression, violence, general offending, and mental illness remain poorly understood, and the literature on this topic is sparse.
This Topic aims to compile scientific papers employing advanced machine learning-boosted statistical analysis to gather significant findings for forensic psychiatry and everyday clinical practice in general psychiatric institutions. We seek scientific contributions examining machine learning analyses of forensic psychiatric relevant topics, including:
- violence and aggression
- general offending
- coercive measures
- risk assessment, prediction and recidivism
- basic research including neuroimaging and genetics
- In- and outpatient treatment in forensic-psychiatric institutions
- psychiatry in prisons
- analysis of medical records
- ethical aspects of ML and forensic psychiatry
Data examined may include medical records, imaging, genetic analysis, juridical expert opinions, and other clinical assessments. We are looking for original research, reviews, and critical comments (e.g., opinions on predictive applicability) on using these modern statistical analyses in this sensitive area.
The rapid increase in data sample sizes in psychiatry and psychology makes modern statistical analysis methods increasingly crucial in these fields. Machine learning (ML) offers the possibility of identifying complex relationships within large and complex data sets and supporting scientific work. Also, within the specific subfield of forensic psychiatry, the abundance of such datasets is increasing rapidly. In the meanwhile, publications performing complex analyses via ML can be observed, especially in forensic-psychiatric risk prediction. Although this offers exciting opportunities, the links between aggression, violence, general offending, and mental illness remain poorly understood, and the literature on this topic is sparse.
This Topic aims to compile scientific papers employing advanced machine learning-boosted statistical analysis to gather significant findings for forensic psychiatry and everyday clinical practice in general psychiatric institutions. We seek scientific contributions examining machine learning analyses of forensic psychiatric relevant topics, including:
- violence and aggression
- general offending
- coercive measures
- risk assessment, prediction and recidivism
- basic research including neuroimaging and genetics
- In- and outpatient treatment in forensic-psychiatric institutions
- psychiatry in prisons
- analysis of medical records
- ethical aspects of ML and forensic psychiatry
Data examined may include medical records, imaging, genetic analysis, juridical expert opinions, and other clinical assessments. We are looking for original research, reviews, and critical comments (e.g., opinions on predictive applicability) on using these modern statistical analyses in this sensitive area.
