以知识为导向的生成性人工智能用于自动化分类学,从药物标签中学习.
Yilu Fang1, Patrick Ryan1,2, Chunhua Weng1
1Department of Biomedical Informatics, Columbia University, New York, NY 10032, United States.
概括
生成型人工智能和现实世界的证据 (RWE) 从标签上构建了一个药物指示分类. 大型语言模型擅长概念层次结构,但难以处理术语关系,这对人类来说也是一个挑战.
科学领域:
- 计算语言学计算语言学
- 生物信息学是一种生物信息学.
- 人工智能在医学中的应用
背景情况:
- 药品标签包含关键但非结构化的指示信息.
- 自动提取和组织这些信息对于药物发现和药物监管至关重要.
- 目前用于分类结构的现有方法通常是手动的,耗时的.
研究的目的:
- 开发一种自动化方法来构建药物指示分类.
- 为了实现这一任务,利用生成型人工智能 (AI),特别是像GPT-4这样的大型语言模型 (LLM) 和现实世界的证据 (RWE).
- 评估人工智能驱动的分类学与领域专家预期的性能.
主要方法:
- 使用GPT-4从46,421个药物标签中提取了2909个药物指示术语.
- 使用与RWE集成的GPT-4代生成的指示概念和推断的附属关系.
- 构建了一个分层的药物指示分类.
- 与心血管,内分泌和生殖尿路疾病的领域专家进行了定量和定性评估.
主要成果:
- 创建了一个药物指示分类法,包含24个高级分类和详细的分类法 (例如,心血管疾病分类法中的242个概念).
- 该分类法涵盖了234个与189种药物相关的指示术语.
- 在确定药物指示层次的过程中,GPT-4的准确度达到>0.7,具有良好的评价者间可靠性.
- 概念与期货的附属关系检查显示出相当可靠到适度可靠性.
结论:
- 生成型人工智能 (LLM) 和RWE可以成功地创建与专家预期一致的药物指示分类.
- 法律法师擅长推导概念层次结构,但在自由文本标签中确定概念与术语的附属关系方面面临挑战.
- 人类专家面临的与检查镜子有关的限制.
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