Detecting at-risk mental states for psychosis (ARMS) using machine learning ensembles and facial features

AIMS: Our study aimed to develop a machine learning ensemble to distinguish “at-risk mental states for psychosis” (ARMS) subjects from control individuals from the general population based on facial data extracted from video-recordings. METHODS: 58 non-help-seeking medication-naïve ARMS and 70 healt...

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Detalles Bibliográficos
Autores principales: Loch, Alexandre Andrade, Gondim, João Medrado, Argolo, Felipe Coelho, Lopes-Rocha, Ana Caroline, Andrade, Julio Cesar, van de Bilt, Martinus Theodorus, de Jesus, Leonardo Peroni, Haddad, Natalia Mansur, Cecchi, Guillermo A., Mota, Natalia Bezerra, Gattaz, Wagner Farid, Corcoran, Cheryl Mary, Ara, Anderson
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Elsevier Science Publisher B. V 2023
Materias:
Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10448183/
https://www.ncbi.nlm.nih.gov/pubmed/37473667
http://dx.doi.org/10.1016/j.schres.2023.07.011

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10448183/
https://www.ncbi.nlm.nih.gov/pubmed/37473667
http://dx.doi.org/10.1016/j.schres.2023.07.011

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