评估一个LLM-Powered系统的潜力,以提高FHIR资源验证
Parinaz Tabari1, Alfonso Piscitelli1, Gennaro Costagliola1
1Department of Informatics, University of Salerno, Italy.
Studies in health technology and informatics
|May 17, 2025
概括
将大型语言模型 (LLM) 与语法验证器相结合,可以显著提高从临床文本生成健康数据标准 (FHIR) 资源的准确性. 少数射击提示方法实现了最高的语法有效性和语义准确性.
科学领域:
- 医疗信息学 医疗信息学
- 人工智能在医学中的应用
- 自然语言处理自然语言处理.
背景情况:
- 大型语言模型 (LLM) 越来越多地被医疗保健专业人员用于人工智能驱动的交互.
- 法律学可以处理广泛的临床数据,包括患者叙述,以支持决策.
- 从非结构化文本中生成标准化的医疗保健数据资源仍然是一个挑战.
研究的目的:
- 评估语法验证器与LLM相结合的有效性,以从自然语言中生成准确的FHIR资源.
- 在此任务中,比较零射击,一射击和少数射击提示策略的性能.
主要方法:
- 利用LLM来将自然语言句子处理成FHIR资源.
- 实现了一个语法验证器,以确保LLM输出符合FHIR语法要求.
- 对比零射击,一射击和少数射击提示技术用于资源生成.
主要成果:
- 一次拍摄和几次拍摄的提示实现了96%的语法有效性,超过了零拍摄 (90%).
- 一次性提示产生了最高的语义准确性,每个FHIR资源的正确比较为25.82.
- 随后的少数射击提示有25.50的正确比较,而零射击则有15.21.
结论:
- 一个语法验证器显著提高了LLM生成的FHIR资源的准确性.
- 短拍提示策略在语法有效性和语义准确性方面都提供了卓越的性能,用于FHIR资源生成.
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