SynthMedic:使用大型语言模型来生成合成排放总结,纠正和验证
Georgi Grazhdanski1, Vasil Vasilev2, Sylvia Vassileva1
1Faculty of Mathematics and Informatics, Sofia University St. Kliment Ohridski, 5 James Bourchier Blvd., Sofia, 1164, Bulgaria.
Journal of biomedical informatics
|September 17, 2025
概括
研究人员开发了一种方法,使用大型语言模型 (LLM) 创建合成临床出院摘要. 这种方法确保了数据隐私,并且可以在没有真实患者信息的情况下用于训练AI.
科学领域:
- 医疗信息学 医疗信息学
- 自然语言处理自然语言处理.
- 医疗保健中的人工智能
背景情况:
- 合成临床文本为训练人工智能模型提供了一个保护隐私的替代方案,而不是真实患者数据.
- 目前用于生成合成医疗数据的方法在确保事实准确性和临床相关性方面面临挑战.
- 合成数据的使用可以提高透明度,减少偏见,降低医疗AI开发的成本.
研究的目的:
- 开发和验证一种方法来生成,验证和纠正使用LLM的合成排放摘要.
- 为训练机器学习模型创建高质量的,公开可用的合成排放摘要集体.
- 确保合成出院总结的医学事实正确性和临床可信度.
主要方法:
- 使用大型语言模型 (LLM) 来生成基于特定疾病和医疗参考 (默克手册) 的合成出院摘要.
- 采用基于LLM和人类专家验证来评估生成的摘要的质量和准确性.
- 实施基于知识图的方法进行自动校正,以确保医疗事实准确性.
主要成果:
- 人类专家的评估证实了合成释放总结的可信性和事实准确性,当它们与医学参考相关时.
- 从医疗专业人员获得了94.35%的系统可用性得分,并从LLM获得了93.65%的忠诚度得分.
- 生成的数据库包括900个综合性释放总结,针对九种重大疾病.
结论:
- 提出的方法有效地产生高质量的合成排放摘要,适合训练AI模型.
- 公开可用的语料库和方法使研究社区能够在不使用敏感患者数据的情况下开发先进的医疗AI.
- 这项工作促进了人工智能工具的开发,以支持医疗保健专业人员完成日常任务.
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