Applicability of two violence risk assessment tools in a psychiatric prison hospital population

Krebs, Julia; Negatsch, Vincent; Berg, C. J.; Aigner, Annette; Opitz‐Welke, Annette; Seidel, Peter; Konrad, Norbert; Voulgaris, Alexander

doi:10.1002/bsl.2474

Cited by 7 publications

(6 citation statements)

References 36 publications

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“…Web-based diagnostics have been widely utilized by researchers to detect risks and facilitate decision making in a range of contexts, including the prediction of chronic disease [33,34], violent behavior [35], self-care [36], and preventive medicine [37]. Therefore, the objective of our work is to design and implement a web-based cancer screening tool that will aid the medical team in making screening decisions.…”

Section: Management Implicationsmentioning

confidence: 99%

Predicting Breast Cancer from Risk Factors Using SVM and Extra-Trees-Based Feature Selection Method

et al. 2022

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Developing a prediction model from risk factors can provide an efficient method to recognize breast cancer. Machine learning (ML) algorithms have been applied to increase the efficiency of diagnosis at the early stage. This paper studies a support vector machine (SVM) combined with an extremely randomized trees classifier (extra-trees) to provide a diagnosis of breast cancer at the early stage based on risk factors. The extra-trees classifier was used to remove irrelevant features, while SVM was utilized to diagnose the breast cancer status. A breast cancer dataset consisting of 116 subjects was utilized by machine learning models to predict breast cancer, while the stratified 10-fold cross-validation was employed for the model evaluation. Our proposed combined SVM and extra-trees model reached the highest accuracy up to 80.23%, which was significantly better than the other ML model. The experimental results demonstrated that by applying extra-trees-based feature selection, the average ML prediction accuracy was improved by up to 7.29% as contrasted to ML without the feature selection method. Our proposed model is expected to increase the efficiency of breast cancer diagnosis based on risk factors. In addition, we presented the proposed prediction model that could be employed for web-based breast cancer prediction. The proposed model is expected to improve diagnostic decision-support systems by predicting breast cancer disease accurately.

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Section: Management Implicationsmentioning

confidence: 99%

Predicting Breast Cancer from Risk Factors Using SVM and Extra-Trees-Based Feature Selection Method

et al. 2022

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“…Previous studies have shown the effectiveness and usefulness of web-based risk assessments in identifying risks and assisting the decision-making for pediatric readmission prediction [34], preventive medicine [35], violent behavior prediction [36], and trauma therapy [37]. Thus, in this section, we aim to design and develop web-based self-care prediction application (web-app) to provide decision tools for therapist in diagnosing children with disabilities.…”

Section: Practical Application Of the Proposed Self-care Prediction Mmentioning

confidence: 99%

“…As suggested by Demšar [33], a statistical significance test can be utilized to prove the significance of the proposed model as compared to other classification models. Furthermore, previous studies have also reported the effectiveness and usefulness of the practical application of prediction model to identify risks and assist the decision-making for pediatric readmission prediction [34], preventive medicine [35], violent behavior prediction [36], and trauma therapy [37].…”

Section: Introductionmentioning

confidence: 99%

A Self-Care Prediction Model for Children with Disability Based on Genetic Algorithm and Extreme Gradient Boosting

et al. 2020

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Detecting self-care problems is one of important and challenging issues for occupational therapists, since it requires a complex and time-consuming process. Machine learning algorithms have been recently applied to overcome this issue. In this study, we propose a self-care prediction model called GA-XGBoost, which combines genetic algorithms (GAs) with extreme gradient boosting (XGBoost) for predicting self-care problems of children with disability. Selecting the feature subset affects the model performance; thus, we utilize GA to optimize finding the optimum feature subsets toward improving the model’s performance. To validate the effectiveness of GA-XGBoost, we present six experiments: comparing GA-XGBoost with other machine learning models and previous study results, a statistical significant test, impact analysis of feature selection and comparison with other feature selection methods, and sensitivity analysis of GA parameters. During the experiments, we use accuracy, precision, recall, and f1-score to measure the performance of the prediction models. The results show that GA-XGBoost obtains better performance than other prediction models and the previous study results. In addition, we design and develop a web-based self-care prediction to help therapist diagnose the self-care problems of children with disabilities. Therefore, appropriate treatment/therapy could be performed for each child to improve their therapeutic outcome.

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“…For years, forensic psychology research has been concerned with demonstrating the reliability and predictive validity of violence risk assessment tools [ 24 , 25 , 26 , 27 , 28 , 29 , 30 ]. Currently, there are more than 200 violence risk assessment tools with a wide range of applications and varying levels of accuracy [ 31 , 32 ].…”

Section: Introductionmentioning

confidence: 99%

Violence Risk Assessment and Risk Management: Case-Study of Filicide in an Italian Woman

Sorge

Borrelli

Saita

et al. 2022

IJERPH

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Background: At an international level, the risk assessment and management process of violent offenders follows a standard method that implies well-defined theoretical models and the use of scientifically validated tools. In Italy, this process is still highly discretionary. The aim of this study is to highlight the advantages deriving from the use of risk assessment tools within the framework of a single case study; Methods: Recidivism risk and social dangerousness of an Italian woman perpetrator of filicide were assessed through the administration of the Level of Service/Case Management Inventory (LS/CMI) instrument supported by Historical Clinical Risk-20 Version 3 (HCR-20 V3); Results: The administration of LS/CMI showed that, in this single case, the subcomponents represent a criminogenic risk/need factor are: Family/Marital, Companions, Alcohol and Drug Problem and Leisure; while constituting strengths: employment and the absence of a Pro-criminal Orientation and an Antisocial Pattern; Conclusions: Data collected through LS/CMI indicated life areas of a single case, which should be emphasised not only to assess the risk of re-offending and social dangerousness but also for a social rehabilitation programme more suited to the subject. This study demonstrates that the LS/CMI assessment tool is suitable for the Italian context.

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Applicability of two violence risk assessment tools in a psychiatric prison hospital population

Cited by 7 publications

References 36 publications

Predicting Breast Cancer from Risk Factors Using SVM and Extra-Trees-Based Feature Selection Method

Predicting Breast Cancer from Risk Factors Using SVM and Extra-Trees-Based Feature Selection Method

A Self-Care Prediction Model for Children with Disability Based on Genetic Algorithm and Extreme Gradient Boosting

Violence Risk Assessment and Risk Management: Case-Study of Filicide in an Italian Woman

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