MED-ChatGPT CoPilot:一个ChatGPT医疗助理用于案例挖掘和辅助治疗
Wei Liu1,2, Hongxing Kan1,2, Yanfei Jiang1
1School of Medical Information Engineering, Anhui University of Traditional Chinese Medicine, Hefei, Anhui, China.
Frontiers in medicine
|October 31, 2024
概括
使用像ChatGPT这样的大型语言模型进行快速工程,可以实现高效的医疗文本数据挖掘. 这种方法增强了诊断和治疗建议,改善了原始模型的性能.
科学领域:
- 人工智能的人工智能
- 自然语言处理自然语言处理.
- 医疗信息学 医疗信息学
背景情况:
- 像GPT-4-1106-preview这样的大型语言模型 (LLM) 提供了先进的文本处理功能.
- 快速工程使得有效的文本数据挖掘能够在没有重新训练模型的情况下实现.
研究的目的:
- 应用快速工程和文本向量化用于医疗文本数据挖掘.
- 使用提取的数据构建一个本地医学知识库.
- 开发一个用于诊断和治疗建议的聊天机器人.
主要方法:
- 快速工程被用来指导ChatGPT挖掘306个医学论文.
- 提取的数据被向量化并存储在本地知识库中.
- 朗链框架将知识库与ChatGPT集成,以创建一个聊天机器人.
主要成果:
- 从241个经过验证的医疗病例数据输入中构建了一个本地医学知识库.
- 增强的ChatGPT模型在医疗问题上显示了7.90%的性能改善.
结论:
- 通过快速的工程,ChatGPT有效地挖掘大规模的医疗文本.
- 未来的工作包括扩大知识库和增强医疗应用.
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