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人工智能和心理健康:对诊断分类进行训练的监督机器学习模型的评估

Anna van Oosterzee¹

¹Utrecht University, Utrecht, The Netherlands.

AI & society

|September 2, 2025

概括

机器学习 (ML) 在精神病学中表现有前途,但与当前的诊断类别相扎. 专注于预后和治疗选择,而不是诊断,为改善患者的结果提供了更大的潜力.

科学领域:

精神病学
人工智能
计算神经科学

背景情况:

机器学习 (ML) 越来越多地应用于精神病学,
目前的ML模型通常依赖于精神疾病诊断和统计手册 (DSM) 的类别.
DSM类别具有已知的局限性,包括异质性和低预测有效性,影响精神病诊断.

研究的目的:

批判性地评估监督的ML模型在模仿临床医生的精神判断方面的局限性.
认为目前在精神病学中的ML应用,专注于DSM分类,为患者提供有限的附加值.
建议将ML的重点转向改善心理健康的预后,治疗选择和预防.

主要方法:

对精神病学数据应用的监督ML技术的批判性分析.
评估训练数据有效性 (DSM类别) 对ML模型性能的影响.
在精神病学中重新定位ML应用的概念框架.

主要成果:

在DSM分类上训练的监督ML模型继承了这些类别的有效性问题.
预测DSM分类的ML模型的高准确性是误导性的,并不能验证分类本身.
由于固有的诊断局限性,目前的ML方法对患者的治疗结果提供了很少的可证明的附加值.

结论:

模仿基于DSM类别的临床医生判断的ML模型在改善精神病患者的结果方面具有有限的效用.
需要一个模式转变,将学习机器专注于跨诊断目标,如预后,治疗选择和预防.
重新定位ML以实现这些目标可以提高个性化治疗策略,并更好地支持临床医生在心理健康保健.

关键词:

美国基本的真相精准精神病学监督机器学习有效性

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