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

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

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

Abstract

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) with a variety of combinations of.modalities (clinical features, cognition, structural magnetic resonance imaging, eventrelated.potentials, and polyunsaturated fatty acids) to predict future psychosis onset. While single-modality SVMs showed a poor to fair accuracy, multi-modal SVMs revealed better predictions, up to 0.88 of the balanced accuracy, suggesting the advantage of multi-modal machine-learning methods for forecasting psychosis onset in ARMS.

Original languageEnglish
Article number100089
JournalBiomarkers in Neuropsychiatry
Volume10
DOIs
StatePublished - 2024/06

Keywords

  • Biomarker
  • Machine-learning
  • Muti-modal
  • Psychotic disorders

ASJC Scopus subject areas

  • Clinical Neurology
  • Clinical Biochemistry
  • Psychiatry and Mental health
  • Biochemistry, medical

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