机器学习可以预测情感和非情感精神病的治疗结果吗? 一个系统的审查和元分析
Monopoli Camilla1, Colombo Federica1, Cazzella Tommaso2
1Psychiatry and Clinical Psychobiology, Division of Neuroscience, IRCCS Ospedale San Raffaele, Milano, Italy.
Neuroscience and biobehavioral reviews
|September 6, 2025
概括
机器学习 (ML) 可以预测患有精神病的治疗反应,准确度为80%. 脑电图数据显示出最高的预测能力,但对于临床使用,研究质量需要改进.
科学领域:
- 精神病学和计算神经科学
- 使用先进的计算方法来了解和治疗精神疾病.
- 专注于机器学习和情感/非情感精神病的交集.
背景情况:
- 针对精神病的个性化干预对于改善患者的结果至关重要.
- 确定治疗反应的可靠预测因素仍然是一个重大的临床挑战.
- 机器学习 (ML) 提供了分析复杂数据以预测治疗疗效的潜力.
研究的目的:
- 使用ML算法系统审查和分析研究,以预测情感和非情感精神病的治疗反应.
- 评估ML模型在预测症状改善,认知和生活质量的整体准确性.
- 确定影响这些ML模型准确性的关键预测因素和因素.
主要方法:
- 在MEDLINE,Web of Science和PsycINFO数据库中进行系统的文献搜索,直到2024年2月.
- 对51项研究进行随机效应元分析,以估计整体预测准确度.
- 分组分析和元回归以评估各种因素的影响 (例如输入特征,处理,ML算法,研究质量).
主要成果:
- 在预测精神病的治疗反应方面,ML模型的整体准确率为80%.
- 脑电图 (EEG) 数据显示预测精度最高 (88%),其次是组合治疗 (85%).
- 预测准确度的显著变化与输入特征和治疗类型有关,观察到高异质性 (I2=0. 89).
结论:
- 机器学习是预测情感和非情感精神病的治疗结果的有希望的工具.
- 脑电图数据和组合治疗显示出强大的预测投入.
- 改善研究方法,提高可重现性,解决偏差高风险对于临床转化至关重要.
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