大型语言模型解构了诊断自闭症背后的临床直觉
Jack Stanley1, Emmett Rabot2, Siva Reddy3
1Mila - Québec Artificial Intelligence Institute, Montréal, QC H2S3H1, Canada; The Neuro - Montréal Neurological Institute (MNI), McConnell Brain Imaging Centre, Department of Biomedical Engineering, Faculty of Medicine, School of Computer Science, McGill University, Montréal, QC H3A2B4, Canada.
Cell
|March 27, 2025
概括
大型语言模型 (LLM) 分析临床报告以了解自闭症诊断. 这项研究将刻板印象的行为和特殊兴趣确定为关键诊断指标,
科学领域:
- 神经科学
- 人工智能
- 临床心理学
背景情况:
- 目前使用全基因组测试或脑部扫描的自闭症谱系障碍 (ASD) 诊断方法取得了有限的成功.
- 医疗保健专业人员的临床直觉仍然是诊断自闭症的主要方法,突出了客观诊断工具的差距.
研究的目的:
- 通过深度学习解构和分析专家诊断自闭症的直觉背后的逻辑.
- 在临床报告中确定有助于准确诊断自闭症的关键指标.
主要方法:
- 在一般文本上进行预训练,并对4000多份临床健康记录进行了微调.
- 在LLM架构中使用可解释性策略来确定驱动诊断决策的突出句子.
主要成果:
- 精细调整的LLM成功地区分了确诊和疑似的自闭症病例.
- 该框架确定了刻板印象的重复行为,特殊兴趣和基于感知的行为作为自闭症的关键诊断指标.
- 这些发现挑战了目前对社会互动缺陷的诊断重视.
结论:
- 临床报告的深度学习分析可以有效地捕获和审视专家的诊断直觉.
- 这项研究表明需要修订目前的自闭症诊断标准,特别是精神障碍诊断和统计手册第五版 (DSM-5),以更好地纳入行为和感知因素.
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