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

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...

Full description

Bibliographic Details
Main Authors: 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
Format: Article
Language:English
Published: Elsevier 2024-06-01
Series:Biomarkers in Neuropsychiatry
Subjects:
Biomarker
Psychotic disorders
Muti-modal
Machine-learning
Online Access:http://www.sciencedirect.com/science/article/pii/S2666144624000078

Internet

http://www.sciencedirect.com/science/article/pii/S2666144624000078

Similar Items