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      Interactions Between Offender and Crime Characteristics Leading to a Lethal Outcome in Cases of Sexually-Motivated Abductions

      research-article
      1 , , 2
      Sexual Abuse
      SAGE Publications
      abduction, sexual assault, lethal outcome, sexual homicide, neural network analysis

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          Abstract

          Despite the widespread public concern regarding abduction, research on this type of crime is scarce. This lack of research is even more pronounced when looking at cases that end with the death of the victim. In fact, all of the research looking at lethal outcomes in cases of abductions has focused exclusively on child victims and has failed to consider the interactions at the multivariate level between the factors related to the death of the victim. Therefore, the aim of the study is to identify offender and crime characteristics – as well as their interactions – associated with a lethal outcome in sexually-motivated abductions using a combination of logistic regression and neural network analyses on a sample of 281 cases (81 cases ending with a lethal outcome, random sample of 200 comparison cases). Findings show that sexually-motivated abductions ending with a lethal outcome are more likely to be characterized by an offender who is a loner, forensically aware, and who who uses a weapon and restraints, and who sexually penetrates and beats a known victim. The neural network analysis show that three different pathways lead to a lethal outcome in sexually-motivated abductions. Such findings are important for correctional practices.

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          Most cited references57

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          Advantages and disadvantages of using artificial neural networks versus logistic regression for predicting medical outcomes.

          J V Tu (1996)
          Artificial neural networks are algorithms that can be used to perform nonlinear statistical modeling and provide a new alternative to logistic regression, the most commonly used method for developing predictive models for dichotomous outcomes in medicine. Neural networks offer a number of advantages, including requiring less formal statistical training, ability to implicitly detect complex nonlinear relationships between dependent and independent variables, ability to detect all possible interactions between predictor variables, and the availability of multiple training algorithms. Disadvantages include its "black box" nature, greater computational burden, proneness to overfitting, and the empirical nature of model development. An overview of the features of neural networks and logistic regression is presented, and the advantages and disadvantages of using this modeling technique are discussed.
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            Artificial neural networks: opening the black box.

            Artificial neural networks now are used in many fields. They have become well established as viable, multipurpose, robust computational methodologies with solid theoretic support and with strong potential to be effective in any discipline, especially medicine. For example, neural networks can extract new medical information from raw data, build computer models that are useful for medical decision-making, and aid in the distribution of medical expertise. Because many important neural network applications currently are emerging, the authors have prepared this article to bring a clearer understanding of these biologically inspired computing paradigms to anyone interested in exploring their use in medicine. They discuss the historical development of neural networks and provide the basic operational mathematics for the popular multilayered perceptron. The authors also describe good training, validation, and testing techniques, and discuss measurements of performance and reliability, including the use of bootstrap methods to obtain confidence intervals. Because it is possible to predict outcomes for individual patients with a neural network, the authors discuss the paradigm shift that is taking place from previous "bin-model" approaches, in which patient outcome and management is assumed from the statistical groups in which the patient fits. The authors explain that with neural networks it is possible to mediate predictions for individual patients with prevalence and misclassification cost considerations using receiver operating characteristic methodology. The authors illustrate their findings with examples that include prostate carcinoma detection, coronary heart disease risk prediction, and medication dosing. The authors identify and discuss obstacles to success, including the need for expanded databases and the need to establish multidisciplinary teams. The authors believe that these obstacles can be overcome and that neural networks have a very important role in future medical decision support and the patient management systems employed in routine medical practice. Copyright 2001 American Cancer Society.
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              Opening the black box of neural networks: methods for interpreting neural network models in clinical applications

              Artificial neural networks (ANNs) are powerful tools for data analysis and are particularly suitable for modeling relationships between variables for best prediction of an outcome. While these models can be used to answer many important research questions, their utility has been critically limited because the interpretation of the “black box” model is difficult. Clinical investigators usually employ ANN models to predict the clinical outcomes or to make a diagnosis; the model however is difficult to interpret for clinicians. To address this important shortcoming of neural network modeling methods, we describe several methods to help subject-matter audiences (e.g., clinicians, medical policy makers) understand neural network models. Garson’s algorithm describes the relative magnitude of the importance of a descriptor (predictor) in its connection with outcome variables by dissecting the model weights. The Lek’s profile method explores the relationship of the outcome variable and a predictor of interest, while holding other predictors at constant values (e.g., minimum, 20th quartile, maximum). While Lek’s profile was developed specifically for neural networks, partial dependence plot is a more generic version that visualize the relationship between an outcome and one or two predictors. Finally, the local interpretable model-agnostic explanations (LIME) method can show the predictions of any classification or regression, by approximating it locally with an interpretable model. R code for the implementations of these methods is shown by using example data fitted with a standard, feed-forward neural network model. We offer codes and step-by-step description on how to use these tools to facilitate better understanding of ANN.

                Author and article information

                Journal
                Sex Abuse
                Sex Abuse
                spsax
                SAX
                Sexual Abuse
                SAGE Publications (Sage CA: Los Angeles, CA )
                1079-0632
                1573-286X
                30 October 2023
                October 2024
                : 36
                : 7
                : 774-798
                Affiliations
                [1 ]Ringgold 1763, universitySimon Fraser University; , Burnaby, BC, Canada
                [2 ]University of Lausanne , Switzerland
                Author notes
                [*]Eric Beauregard, School of Criminology, Simon Fraser University, 8888 University Drive, Burnaby, BC V5A 1S6, Canada. Email: ebeaureg@ 123456sfu.ca
                Author information
                https://orcid.org/0000-0003-1828-0891
                Article
                10.1177_10790632231210536
                10.1177/10790632231210536
                11425975
                37902157
                58d6e83d-104c-4f6a-871d-b25a255ce80a
                © The Author(s) 2023

                This article is distributed under the terms of the Creative Commons Attribution-NonCommercial 4.0 License ( https://creativecommons.org/licenses/by-nc/4.0/) which permits non-commercial use, reproduction and distribution of the work without further permission provided the original work is attributed as specified on the SAGE and Open Access page ( https://us.sagepub.com/en-us/nam/open-access-at-sage).

                History
                : 23 April 2023
                : 21 September 2023
                : 4 October 2023
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                Sexual medicine
                abduction,sexual assault,lethal outcome,sexual homicide,neural network analysis
                Sexual medicine
                abduction, sexual assault, lethal outcome, sexual homicide, neural network analysis

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