التفاصيل البيبلوغرافية
| العنوان: |
Prediction of psychotic disorder in individuals with clinical high-risk state by multimodal machine-learning: A preliminary study |
| المؤلفون: |
Takayanagi, Yoichiro, Sasabayashi, Daiki, Takahashi, Tsutomu, Higuchi, Yuko, Nishiyama, Shimako, Tateno, Takahiro, Mizukami, Yuko, Akasaki, Yukiko, Furuichi, Atsushi, Kobayashi, Haruko, Takayanagi, Mizuho, Noguchi, Kyo, Tsujii, Noa, Suzuki, Michio |
| المصدر: |
Biomarkers in Neuropsychiatry; June 2024, Vol. 10 Issue: 1 |
| مستخلص: |
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. |
| قاعدة البيانات: |
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