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机器学习模型的开发和验证,以利用临床和神经成像数据预测痴迷强迫症患者的认知行为治疗结果
Laurens A van de Mortel1,2, Willem B Bruin1,2,3, Pino Alonso4,5,6
1Amsterdam UMC location University of Amsterdam, Department of Psychiatry, Meibergdreef 9, Amsterdam, The Netherlands.
medRxiv : the preprint server for health sciences
|February 24, 2025
概括
在认知行为疗法 (CBT) 后预测强迫症 (OCD) 的缓解是具有挑战性的. 临床数据,而不是神经成像,在机器学习模型中显示出预测强迫症患者治疗缓解的前景.
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.
- 医疗信息学 医疗信息学
背景情况:
- 强迫症 (OCD) 是一种使人虚弱的心理健康状况.
- 认知行为疗法 (CBT) 是主要的治疗方法,但预测患者的反应仍然很困难.
- 多中心神经成像和临床数据为预测建模提供了潜力.
研究的目的:
- 开发和比较机器学习模型,用于预测强迫症患者的CBT反应和缓解.
- 评估临床数据与静止状态功能磁共振成像 (rs-fMRI) 数据的预测效用.
- 确定强迫症治疗结果的关键临床和神经影像预测因素.
主要方法:
- 利用来自ENIGMA-OCD联盟的159名成年强迫症患者的基线临床和rs-fMRI数据.
- 计算的rs-fMRI指标包括低频波动的分数振幅和区域均性.
- 应用支持矢量机和随机森林分类器,以使用临床数据,rs-fMRI数据或两者预测缓解和反应.
主要成果:
- 仅仅临床数据就实现了预测缓解的AUC为0.69 (p=0.001),症状严重程度较低,年龄较小和高等教育是显著因素.
- 从rs-fMRI数据的区域同质性产生了最好的缓解预测性能 (AUC=0.59).
- rs-fMRI数据通常没有改善治疗响应的机会水平以外的预测.
结论:
- 使用临床数据的机器学习模型显示了在接受CBT的强迫症患者中预测缓解的潜力.
- 多中心 rs-fMRI 数据对强迫症认知治疗结果的预测能力似乎有限.
- 进一步的研究可能会专注于改进临床预测因子,并探索其他神经影像模式.
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