使用大型语言模型嵌入的精神疾病相关自发通信的算法分类:算法开发和验证.
Ryan Allen Shewcraft1, John Schwarz1, Mariann Micsinai Balan1
1Department of Global Biometrics & Data Sciences, Bristol Myers Squibb, 3551 Lawrenceville Rd, Lawrence Township, NY, 08648, United States, 1 800-332-2056.
大型语言模型 (LLM) 可以分析语言模式,以帮助区分精神疾病. 这项研究表明,LLM为诊断和监测ADHD和BPD等疾病提供了客观的语言标记.
科学领域:
- 计算语言学计算语言学
- 精神病诊断诊断精神病学诊断的方法
- 医疗保健中的人工智能
背景情况:
- 语言是人类沟通的关键,受思想和情绪的影响.
- 精神疾病会影响认知和情绪过程,改变语言使用.
- 大型语言模型 (LLM) 为精神病学中的客观语言分析提供了潜力.
研究的目的:
- 探索LLM用于分析自发沟通以区分精神疾病.
- 为了证明LLM嵌入式识别独特的语言标记来分类7种精神疾病.
- 评估LLM在精神疾病诊断和监测中的实用性.
主要方法:
- 利用来自70亿参数LLM的嵌入来分析来自7个精神疾病子网格的37,000多条帖子.
- 在LLM嵌入上接受过交叉验证的极端梯度提升分类器的培训.
- 使用统一的多重近似和投影来可视化语言关系.
主要成果:
- 该分类器获得了0.73的平均F1分数和0.95.9的微平均AUC.
- 注意力缺陷/多动症 (ADHD) 帖子显示最高分类AUC (0.97),边缘性人格障碍 (BPD) 则是最低的 (0.89).
- 与其他最先进的嵌入方法相比,生成表示指令调整语言模型-7B表现出更高的性能.
结论:
- 法学士可以客观地分析语言使用,以区分精神疾病.
- 研究结果表明,LLM提供了对特定疾病的语言模式有价值的见解.
- 未来的研究应该验证临床人群中的发现,并探索并发性疾病.
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