دانلود مقاله ISI انگلیسی شماره 43930
عنوان فارسی مقاله

استنتاج علی برای مدیریت ریسک خشونت و حمایت از تصمیم گیری در روانپزشکی قانونی

کد مقاله سال انتشار مقاله انگلیسی ترجمه فارسی تعداد کلمات
43930 2015 14 صفحه PDF سفارش دهید محاسبه نشده
خرید مقاله
پس از پرداخت، فوراً می توانید مقاله را دانلود فرمایید.
عنوان انگلیسی
Causal inference for violence risk management and decision support in forensic psychiatry
منبع

Publisher : Elsevier - Science Direct (الزویر - ساینس دایرکت)

Journal : Decision Support Systems, Volume 80, December 2015, Pages 42–55

کلمات کلیدی
شبکه های بیزی - شبکه اعتقاد - مداخلات علی - جرم شناسی - روانپزشکی قانونی - سلامت روان
پیش نمایش مقاله
پیش نمایش مقاله استنتاج علی برای مدیریت ریسک خشونت و حمایت از تصمیم گیری در روانپزشکی قانونی

چکیده انگلیسی

The purpose of medium secure services (MSS) is to provide accommodation, support, and treatment to individuals with enduring mental health problems who usually come into contact with the criminal justice system. These individuals are, therefore, believed to pose a risk of violence to themselves as well as to other individuals. Assessing and managing the risk of violence is considered to be a critical component for discharged decision making in MSS. Methods for violence risk assessment in this area of research are typically based on regression models or checklists with no statistical composition and which naturally demonstrate mediocre predictive performance and, more importantly, without providing genuine decision support. While Bayesian networks have become popular tools for decision support in the medical field over the last couple of decades, they have not been extensively studied in forensic psychiatry. In this paper, we describe a decision support system using Bayesian networks, which is mainly parameterised based on questionnaire, interviewing and clinical assessment data, for violence risk assessment and risk management in patients discharged from MSS. The results demonstrate moderate to significant improvements in forecasting capability. More importantly, we demonstrate how decision support is improved over the well-established approaches in this area of research, primarily by incorporating causal interventions and taking advantage of the model's ability in answering complex probabilistic queries for unobserved variables.

خرید مقاله
پس از پرداخت، فوراً می توانید مقاله را دانلود فرمایید.