在安全网精神病医院的临床笔记中从临床笔记中进行自杀表型化,使用预先训练的语言模型进行多标签分类
Zehan Li1, Yan Hu1, Scott Lane2
1MacWilliam School of Biomedical Informatics, The University of Texas Health Science Center at Houston, TX, USA.
概括
这项研究表明,语言模型RoBERTa在临床笔记中准确地识别了自杀事件,如想法和尝试. 一个多标签策略改善了它的表现,提高了自杀预防的效率.
科学领域:
- 医疗保健中的人工智能
- 临床自然语言处理 临床自然语言处理
- 心理健康信息学心理健康信息学
背景情况:
- 准确识别自杀事件对于精神病治疗中有效的自杀预防策略至关重要.
- 预训练语言模型 (PLM) 显示出从非结构化的临床文本中提取自杀倾向信息的潜力.
- 现有的方法需要评估,以检测同时发生的自杀事件.
研究的目的:
- 评估基于BERT的语言模型在从临床叙述中识别共存的自杀事件方面的表现.
- 为了比较不同的微调策略来检测自杀念头,自杀企图,接触自杀和非自杀自伤.
- 确定最优的模型和策略,以改善在高急性精神病诊所的自杀风险评估.
主要方法:
- 利用了500个注释精神病学评估笔记,标记了自杀念头 (SI),自杀未遂 (SA),接触自杀 (ES) 和非自杀自伤 (NSSI).
- 通过使用两个微调策略评估了四种基于BERT的模型 (RoBERTa,MentalBERT,BioClinicalBERT,BERT):多个单标签和单个多标签分类.
- 使用精度和F1分数评估模型性能.
主要成果:
- 在二进制相关性 (精度=0.86,F1=0.78) 中,RoBERTa获得了最高的性能.
- 心理BERT (F1=0.74) 和生物临床BERT (F1=0.72) 也表现出显著的性能.
- 用单个多标签分类器微调RoBERTa进一步提高了性能 (精度=0.88,F1=0.81).
结论:
- 相关领域的预训练模型,特别是RoBERTa,在临床笔记中有效识别复杂的自杀事件.
- 单一的多标签分类策略提高了自杀风险检测的效率和性能.
- 这些发现支持使用先进的NLP模型来改善自杀预防措施和护理质量.
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