开放式大型语言模型在德国医疗保健中的临床文档的可行性:现实世界模型评估研究
Felix Heilmeyer1, Daniel Böhringer2, Thomas Reinhard2
1Institute of Digitalization in Medicine, Faculty of Medicine and Medical Center, University of Freiburg, Breisacher Straße 153, Freiburg im Breisgau, 79110, Germany, 49 27039392.
JMIR medical informatics
|August 28, 2024
概括
非专有大型语言模型 (LLM) 可以生成可用的德语医学文本用于文档,即使资源有限. 考虑目标语言的模型选择对于有效的医疗写作辅助至关重要.
科学领域:
- 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) 提供了减少医疗文件处理时间的潜力.
- 商业法学士课程为医疗专业人员带来实际,道德和法律挑战.
研究的目的:
- 评估使用非专有GPT型LLM用于医疗写作辅助的可行性.
- 在有限的计算资源的情况下,在本地环境中生成德国医学文本.
主要方法:
- 训练了四个具有不同架构的70亿参数模型.
- 使用Anthropic的Claude-v2作为自动评分器来评估模型.
- 在真实世界的数据上与人类专家一起评估了表现最好的模型的可用性.
主要成果:
- 在德语文本上从头开始训练的BLOOM-CLP-German在自动化评估中表现最好.
- 人类专家发现,由BLOOM-CLP-German生成的93.1%的报告可以在微小的变化或没有变化的情况下使用.
结论:
- 非专有LLM可以生成合适的医疗文档文本,即使使用有限的计算资源.
- 在LLM选择非英语文本生成时,必须考虑目标语言.
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