使用生成语言模型对死亡证明进行ICD编码
Isabel Coutinho1,2, Gonçalo M Correia3, Bruno Martins1,2
1INESC-ID, Lisbon, Portugal.
PLOS digital health
|February 24, 2026
概括
大型语言模型 (LLM) 对临床文件的国际疾病分类 (ICD) 编码有希望. 生成型LLM实现了与死亡证明编码的编码器模型可比的分类准确性.
科学领域:
- 人工智能的人工智能
- 自然语言处理自然语言处理.
- 医疗信息学 医疗信息学
背景情况:
- 大型语言模型 (LLM) 在文本生成方面表现出色,但在文本分类任务中未得到充分利用.
- 临床文件的国际疾病分类 (ICD) 编码是一个关键但具有挑战性的分类任务.
研究的目的:
- 适应LLaMA生成语言模型用于ICD编码任务.
- 评估生成LLM与ICD编码的传统编码器模型的性能.
主要方法:
- 使用一个用语言建模目标训练的LLaMA生成模型.
- 员工在推断时间被限制解码,而不是歧视性微调.
- 将该模型应用于葡萄牙的死亡证明,以对死亡原因进行编码.
- 与BERT编码器模型进行性能比较.
主要成果:
- 生成式语言模型在死亡证明的ICD编码中取得了强的结果.
- 分类准确度与已建立的编码器模型的分类准确度相当.
- 证明了语言生成对于ICD编码的适用性.
结论:
- 语言生成模型为ICD编码提供了可行的和有效的方法.
- 一个单一的统一生成模型可以处理多个相关的编码任务,例如底层和多个原因编码.
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