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大型语言模型在自杀方法预测中的推断性能和时间稳定性:法医精神病学分析

Halit Canberk Aydogan¹, Hacer Yaşar Teke¹, Muhammet Sevindik²

¹Department of Forensic Medicine, Ordu University Training and Research Hospital, Ordu, Turkey.

Health informatics journal

|January 6, 2026

概括

大型语言模型 (LLM) 显示出从间接精神病学数据预测自杀方法的前景. 双子座 2.5 闪光显示了最高的准确性,尽管需要进一步的细化,为罕见的病例和时间一致性.

科学领域:

法医精神病学法医精神病学
人工智能的人工智能
机器学习机器学习

背景情况:

自杀方法的预测在法医精神病学中至关重要.
间接的精神病学指标为预测建模提供了潜力.
为这些预测评估人工智能 (AI) 工具是一个新兴的领域.

研究的目的:

评估大语言模型 (LLM) 在仅使用间接法医精神病学指标预测自杀方法方面的有效性.
在这个专门的预测任务中比较各种LLM的表现.
评估LLM预测的准确性,精度,回忆,F1得分和时间可重现性.

主要方法:

92个法医精神病学病例 (自杀未遂的幸存者) 的回顾分析.
提取变量:年龄,性别,精神病诊断,尝试史,药物使用,冲动性,意识.
用标准化匿名提示测试六个LLM (OpenAI和Google DeepMind);由盲目的法医医生验证的预测.

主要成果:

双子 2.5 闪光实现了最高的精度 (76.09%),F1得分 (46.9%) 和回忆 (45.2%).
该模型成功预测了常见的方法,如药物过量服用,但在罕见的类别中遇到了困难.
对表现最好的模型观察到适度的时间可重现性 (科恩卡帕=0.582);其他模型表现较差.

结论:

从间接的精神病学数据中推断自杀方法的潜力令人鼓舞.
目前的LLM性能限制包括检测罕见的方法和确保一致的时间预测.
在广泛的法医应用之前,需要进一步的方法开发和外部验证.

关键词:

法医医学中的人工智能法医精神病学评估法医精神病学评估大型语言模型 (LLM)自杀方法预测自杀方法预测人工智能模型中的时间稳定性

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