Patterns of risk—Using machine learning and structural neuroimaging to identify pedophilic offenders

Patterns of risk—Using machine learning and structural neuroimaging to identify pedophilic offenders

BackgroundChild sexual abuse (CSA) has become a focal point for lawmakers, law enforcement, and mental health professionals. With high prevalence rates around the world and far-reaching, often chronic, individual, and societal implications, CSA and its leading risk factor, pedophilia, have been well...

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Detalles Bibliográficos
Autores principales: David Popovic, Maximilian Wertz, Carolin Geisler, Joern Kaufmann, Markku Lähteenvuo, Johannes Lieslehto, Joachim Witzel, Bernhard Bogerts, Martin Walter, Peter Falkai, Nikolaos Koutsouleris, Kolja Schiltz
Formato: Artículo
Lenguaje:English
Publicado: Frontiers Media S.A. 2023-04-01
Colección:Frontiers in Psychiatry
Materias:
pedophilia
MRI
child sexual abuse (CSA)
support vector machines
machine learning
forensic psychiatry
Acceso en línea:https://www.frontiersin.org/articles/10.3389/fpsyt.2023.1001085/full

Internet

https://www.frontiersin.org/articles/10.3389/fpsyt.2023.1001085/full

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