用机器学习方法解构精神病中的认知障碍
Robert A McCutcheon1,2,3, Richard S E Keefe4, Philip M McGuire1,2,5
1Department of Psychiatry, University of Oxford, Oxford, United Kingdom.
JAMA psychiatry
|October 9, 2024
概括
精神病的认知障碍受到一般风险因素的重大影响,而不仅仅是诊断本身. 解决这些跨诊断因素可能会改善不同精神病障碍的认知功能.
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.
- 认知科学 认知科学
背景情况:
- 精神病患者的认知功能受损.
- 包括年龄,性别,教育和童年逆境在内的各种因素与认知功能有关.
- 精神病中的认知缺陷可能源于认知能力差的一般风险因素.
研究的目的:
- 量化精神病中认知障碍在多大程度上归因于对一般风险因素的暴露.
- 为了区分诊断分类与其他暴露对认知功能的影响.
主要方法:
- 一项采用双极精神分裂症网络中介类型 (B-SNIP) 研究数据的横截面研究.
- 用极端梯度增强回归和沙普利增量解释 (SHAP) 值来分析关系的预测建模.
- 包括健康的对照组,精神分裂症患者,精神分裂情绪障碍,双相I型精神障碍与精神病患者及其亲属.
主要成果:
- 预测模型准确地捕获了认知复合分数 (r=0.72).
- 患有精神分裂症,精神分裂情绪障碍和双相I型精神障碍与精神病的个人表现出明显较差的认知得分比对照组.
- 超诊断因素,而不是单独的诊断,占认知障碍的很大一部分,抗精神病剂量也起到了作用.
结论:
- 精神病中认知障碍的很大一部分与一般人群相关的因素有关.
- 对于精神病中的认知缺陷的干预措施可能会从一种诊断无关的,以症状为目标的方法中受益.
- 了解和解决跨诊断风险因素对于管理精神病障碍的认知功能障碍至关重要.
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