一种多变量认知方法,用于预测最近发病的精神病中的社会功能,以应对计算机化认知训练
Nina Walter1, Julian Wenzel1, Shalaila S Haas2
1Department of Psychiatry and Psychotherapy, Faculty of Medicine and University Hospital, University of Cologne, Kerpenerstr.62, 50931, Cologne, Germany.
Progress in neuro-psychopharmacology & biological psychiatry
|September 17, 2023
概括
基线认知数据可以预测在接受认知训练 (CT) 的近期发病精神病 (ROP) 患者的未来社会功能. 虽然预测个人对社会认知训练 (SCT) 的反应需要进一步的研究,但认知测量提供了对社会功能结果的可靠估计.
科学领域:
- 神经科学是一个神经科学.
- 精神病学是一个精神病学.
- 机器学习 机器学习
背景情况:
- 最近发病的精神病 (ROP) 患者表现出显著的认知和社会功能缺陷.
- 认知训练 (CT) 在改善ROP患者的认知和社会功能方面显示出小到中等效果.
- 预测治疗反应异质性对于个性化干预至关重要.
研究的目的:
- 研究基线认知数据的潜力,以预测ROP患者CT后社会功能改善的可能性.
- 开发和验证用于预测社会功能的机器学习模型.
- 评估社会认知训练 (SCT) 对预测社会功能的影响.
主要方法:
- 使用了多变量模式分析 (MVPA) 和支持向量机 (SVM) 分类器.
- 一个SVM分类器被训练在个性化预后工具早期精神病管理 (PRONIA) 研究的数据.
- 该分类器应用于独立的ROP样本进行预测和交叉验证.
主要成果:
- 基线认知数据预测了社会功能,在独立的ROP样本中,均衡准确率 (BAC) 为66.4%.
- 关键预测因素包括数字符号替换测试,连续性能测试和口语流利测试的表现.
- 在干预样本中,分类器在基线达到59.3%的BAC,在随访时达到64.8%.
- 与通常治疗 (TAU) 相比,社会认知训练 (SCT) 组在预测的社会功能方面显著改善.
结论:
- 基线认知数据可以为ROP患者的未来社会功能提供可靠的个人估计.
- 使用认知数据预测个人对SCT的反应,需要在大规模试验中进一步调查.
- 认知数据对指导ROP个性化干预有前途.
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