以人工智能为驱动的方法来创建和评估药物错误的合成数据集
Hanae Touati1, Rafika Thabet2, Franck Fontanili1
1University of Toulouse, Industrial Engineering Center of IMT Mines Albi, Albi 81000, France.
Journal of biomedical informatics
|August 12, 2025
概括
大型语言模型以法语生成现实的合成药物错误 (ME) 数据集. 在这些数据上训练的模型实现了强大的分类性能,支持人工智能驱动的分析,在现实数据稀缺的情况下.
科学领域:
- 医疗信息学 医疗信息学
- 医疗保健中的人工智能
- 自然语言处理自然语言处理.
背景情况:
- 对现实数据的有限访问阻碍了用于药物错误 (ME) 分析的机器学习模型的开发.
- 合成数据生成提供了一个潜在的解决方案,以克服医疗保健中的数据短缺.
研究的目的:
- 通过使用先进的变压器模型,在法语中创建一个全面的药物错误 (ME) 综合数据集.
- 评估合成数据集的现实性和实用性,用于训练ME分类模型.
主要方法:
- 使用变压器模型 (GPT-4,LLAMA3,Mistral) 来生成法语ME报告的多样化合成数据集.
- 医疗保健专业人员进行了专家评估和人工智能驱动的分析,以评估数据集质量和模型性能.
主要成果:
- 合成数据集准确地代表了各种ME场景,并得到了专家评估的证实.
- 在合成数据上训练的机器学习模型在真实世界ME数据上展示了强大的分类性能.
- 使用合成数据的少数射击学习方法从专家评审者那里获得了高的有效率.
结论:
- 大型语言模型可以有效地生成现实的法语合成ME报告.
- 在合成数据上训练的分类器在真实数据上获得了高达0.78的F1分数.
- 在现实数据有限的情况下,合成ME数据是人工智能驱动分析的可行解决方案.
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