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

Q2 Medicine

Biomarkers in Neuropsychiatry Pub Date : 2024-03-21 DOI:10.1016/j.bionps.2024.100089

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

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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.

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通过多模态机器学习预测临床高危人群的精神障碍：初步研究

我们需要能够可靠预测高危精神状态（ARMS）患者精神病转变的客观标记。在这项研究中，我们招募了 65 名 ARMS 受试者（其中 17 人（26.2%）后来患上了精神病），并使用多种模式组合（临床特征、认知、结构性磁共振成像、事件相关电位和多不饱和脂肪酸）的监督线性支持向量机（SVM）来预测未来精神病的发病。单模态 SVM 的预测准确率从较差到一般，而多模态 SVM 的预测准确率更高，达到了均衡准确率的 0.88，这表明多模态机器学习方法在预测 ARMS 患者精神病发病方面具有优势。

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