使用ChatGPT从疾病文本中构建表型实体.
Marcel Luis Palacios-Jaén1, Lucía Prieto-Santamaría1,2, Ernestina Menasalvas1,2
1Escuela Técnica Superior de Ingenieros Informáticos, Universidad Politécnica de Madrid, Spain.
Studies in health technology and informatics
|August 8, 2025
概括
简单的提示有效地从使用ChatGPT的疾病文本中提取表型表现. 虽然没有超过基准,但这种方法为表型数据的生物医学文本挖掘提供了有希望的途径.
科学领域:
- 生物医学信息学 生物医学信息学
- 自然语言处理自然语言处理.
- 人工智能的人工智能
背景情况:
- 从非结构化的生物医学文本中准确地提取表型实体仍然是一个挑战.
- 人工智能的进步,就像大型语言模型一样,为文本挖掘提供了新的可能性.
研究的目的:
- 为了探索ChatGPT 3.5 Turbo的有效性,使用快速工程来从疾病文本中提取表型数据.
- 为了比较不同提示策略与UMLS MetaMap等既有工具的性能.
主要方法:
- 使用ChatGPT 3.5 Turbo处理了百个维基百科疾病文本.
- 测试了各种提示工程技术,包括简单的关键字关注,实体字典,解释步骤和提示链接.
- 结果使用精度,回忆和F1分数进行评估,并与UMLS MetaMap进行基准测试.
主要成果:
- 专注于"表型表现"的更简单提示实现了最高的F1得分0.60.
- 复杂的提示和先进的技术,如一次性示例或提示链接,产生了较低的性能.
- 包括关键词"表现"改善了回忆力.
结论:
- 直接提示策略更有效地利用ChatGPT提取表型数据实体.
- 虽然有希望,但目前ChatGPT对此任务的性能低于UMLS MetaMap基准.
- 需要进一步完善快速工程,以提高生物医学文本挖掘的准确性.
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