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 language | English |
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Article number | 100089 |
Journal | Biomarkers in Neuropsychiatry |
Volume | 10 |
DOIs | |
State | Published - 2024/06 |
Keywords
- Biomarker
- Machine-learning
- Muti-modal
- Psychotic disorders
ASJC Scopus subject areas
- Clinical Neurology
- Clinical Biochemistry
- Psychiatry and Mental health
- Biochemistry, medical