Managing uncertainty in the clinical prediction of risk of harm: Bringing a Bayesian approach to forensic mental health | Zendy

Duggan Conor | Zendy; Jones Roland | Zendy

AI Assistant Blog Pricing

Home ZAIA Blog

Premium

Managing uncertainty in the clinical prediction of risk of harm: Bringing a Bayesian approach to forensic mental health

Author(s) -

Duggan Conor,

Jones Roland

Publication year - 2017

Publication title -

criminal behaviour and mental health

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 0.63

H-Index - 54

eISSN - 1471-2857

pISSN - 0957-9664

DOI - 10.1002/cbm.2031

Subject(s) - harm , bayesian probability , mental health , event (particle physics) , forensic science , psychology , computer science , psychiatry , medicine , artificial intelligence , social psychology , physics , quantum mechanics , veterinary medicine

Predicting the likelihood of harm posed by mentally disordered offenders remains controversial. It is proposed that a Bayesian approach may help quantify the uncertainty surrounding such prediction. An example of this approach quantifying the risk of breast cancer in the event of a positive mammogram is provided. Copyright © 2017 John Wiley & Sons, Ltd.

This content is not available in your region!

Continue researching here.

Having issues? You can contact us here

Accelerating Research