Exploring substance use as rule‐violating behaviour during inpatient treatment of offender patients with schizophrenia

Sonnweber, Martina; Kirchebner, Johannes; Günther, Moritz Philipp; Kappes, Johannes Rene; Lau, Steffen

doi:10.1002/cbm.2245

Cited by 7 publications

(4 citation statements)

References 32 publications

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“…Offences leading to the referenced forensic psychiatric hospitalization included both violent crimes, including (attempted) homicide, assault, violent offences against sexual integrity, robbery, and arson, and non-violent crimes, including threats and coercion, property crime without violence, criminal damage, traffic offences, drug offences, and illegal gun possessions. This patient population has been evaluated regarding other aspects, e.g., inpatient aggression, using a similar methodologic approach, which is why extracts of the following section may be replicated in part [23][24][25][26].…”

Section: Methodsmentioning

confidence: 99%

Correlates of Social Isolation in Forensic Psychiatric Patients with Schizophrenia Spectrum Disorders: An Explorative Analysis Using Machine Learning

Machetanz

Lau

Huber

et al. 2023

IJERPH

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The detrimental effects of social isolation on physical and mental health are well known. Social isolation is also known to be associated with criminal behavior, thus burdening not only the affected individual but society in general. Forensic psychiatric patients with schizophrenia spectrum disorders (SSD) are at a particularly high risk for lacking social integration and support due to their involvement with the criminal justice system and their severe mental illness. The present study aims to exploratively evaluate factors associated with social isolation in a unique sample of forensic psychiatric patients with SSD using supervised machine learning (ML) in a sample of 370 inpatients. Out of >500 possible predictor variables, 5 emerged as most influential in the ML model: attention disorder, alogia, crime motivated by ego disturbances, total PANSS score, and a history of negative symptoms. With a balanced accuracy of 69% and an AUC of 0.74, the model showed a substantial performance in differentiating between patients with and without social isolation. The findings show that social isolation in forensic psychiatric patients with SSD is mainly influenced by factors related to illness and psychopathology instead of factors related to the committed offences, e.g., the severity of the crime.

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Section: Methodsmentioning

confidence: 99%

Correlates of Social Isolation in Forensic Psychiatric Patients with Schizophrenia Spectrum Disorders: An Explorative Analysis Using Machine Learning

Machetanz

Lau

Huber

et al. 2023

IJERPH

Self Cite

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“…In (Sonnweber et al, 2022) [33], ML methods were used to identify the factors that distinguish between hospitalized patients with schizophrenia spectrum disorders who consumed alcohol or an illicit substance during forensic psychiatric hospitalization and those who did not. The database consisted of 364 cases, with demographic, clinical, and criminal data from residents at the Zurich Forensic Psychiatry Hospital from 1982 to 2016.…”

Section: Introductionmentioning

confidence: 99%

Machine Learning Applications to Identify Young Offenders Using Data from Cognitive Function Tests

Bonfante,

Montes,

Pino

et al. 2023

Data

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Machine learning techniques can be used to identify whether deficits in cognitive functions contribute to antisocial and aggressive behavior. This paper initially presents the results of tests conducted on delinquent and nondelinquent youths to assess their cognitive functions. The dataset extracted from these assessments, consisting of 37 predictor variables and one target, was used to train three algorithms which aim to predict whether the data correspond to those of a young offender or a nonoffending youth. Prior to this, statistical tests were conducted on the data to identify characteristics which exhibited significant differences in order to select the most relevant features and optimize the prediction results. Additionally, other feature selection methods, such as Boruta, RFE, and filter, were applied, and their effects on the accuracy of each of the three machine learning models used (SVM, RF, and KNN) were compared. In total, 80% of the data were utilized for training, while the remaining 20% were used for validation. The best result was achieved by the K-NN model, trained with 19 features selected by the Boruta method, followed by the SVM model, trained with 24 features selected by the filter method.

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“…In (Sonnweber et al, 2022) [21], ML methods were used to identify the factors that distinguish between hospitalized patients with schizophrenia spectrum disorders who have confirmed alcohol or illicit substance use during forensic psychiatric hospitalization and those who do not. The database consisted of 364 cases, with demographic, clinical, and criminal data from residents at the Zurich Forensic Psychiatry Hospital from 1982 to 2016.…”

Section: Introductionmentioning

confidence: 99%

Machine Learning Applications to Identify Young Offenders Using Data from Cognitive Function Test

Bonfante,

Montes,

Pino

et al. 2023

Preprint

View full text Add to dashboard Cite

Machine Learning techniques can be used to identify whether deficits in cognitive functions contribute to antisocial and aggressive behavior. This paper initially presents the results of tests conducted on delinquent and non-delinquent youths to assess their cognitive functions. The dataset extracted from these assessments, consisting of 37 predictor variables and one target. was used to train three algorithms that aim to predict whether the data corresponds to that of a young offender or a non-offending youth. Prior to this, statistical tests were conducted on the data to identify characteristics that exhibited significant differences in order to select the most relevant features and optimize the prediction results. Additionally, other feature selection methods, such as Boruta, RFE, and Filter, were applied, and their effects on the accuracy of each of the three machine learning models used (SVM, RF, and KNN) were compared. 80% of the data were utilized for training, while the remaining 20% were used for validation. The best result was achieved by the K-NN model trained with 19 features selected by the Boruta method, followed by the SVM model trained with 24 features selected by the filter method.

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Exploring substance use as rule‐violating behaviour during inpatient treatment of offender patients with schizophrenia

Cited by 7 publications

References 32 publications

Correlates of Social Isolation in Forensic Psychiatric Patients with Schizophrenia Spectrum Disorders: An Explorative Analysis Using Machine Learning

Correlates of Social Isolation in Forensic Psychiatric Patients with Schizophrenia Spectrum Disorders: An Explorative Analysis Using Machine Learning

Machine Learning Applications to Identify Young Offenders Using Data from Cognitive Function Tests

Machine Learning Applications to Identify Young Offenders Using Data from Cognitive Function Test

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