Prediction of psychotic disorder in individuals with clinical high-risk state by multimodal machine-learning: A preliminary study

Objective markers which can reliably predict psychosis transition among individuals with at-risk mental state (ARMS) are warranted. In this study, sixty-five ARMS subjects [of whom 17 (26.2%) later developed psychosis] were recruited, and we performed supervised linear support vector machine (SVM) w...

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Detalhes bibliográficos

Principais autores:	Yoichiro Takayanagi, Daiki Sasabayashi, Tsutomu Takahashi, Yuko Higuchi, Shimako Nishiyama, Takahiro Tateno, Yuko Mizukami, Yukiko Akasaki, Atsushi Furuichi, Haruko Kobayashi, Mizuho Takayanagi, Kyo Noguchi, Noa Tsujii, Michio Suzuki
Formato:	Artigo
Idioma:	Inglês
Publicado em:	Elsevier 2024-06-01
coleção:	Biomarkers in Neuropsychiatry
Assuntos:	Biomarker Psychotic disorders Muti-modal Machine-learning
Acesso em linha:	http://www.sciencedirect.com/science/article/pii/S2666144624000078
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