MedKPL:一个异质的知识增强的快速学习框架,用于可转移的诊断
Yuxing Lu1, Xiaohong Liu2, Zongxin Du3
1Department of Big Data and Biomedical AI, College of Future Technology, Peking University, Beijing 100091, China.
Journal of biomedical informatics
|June 14, 2023
概括
人工智能 (AI) 诊断系统可以通过整合各种医学知识来改进. 我们的医学知识增强快速学习 (MedKPL) 框架增强了临床笔记分类和疾病可转移性.
科学领域:
- 医疗信息学 医疗信息学
- 人工智能在医学中的应用
背景情况:
- 临床诊断依赖于临床医生的专业知识和外部医学知识.
- 当前的人工智能诊断系统在扩展性和整合临床笔记分类的多种知识来源方面扎.
研究的目的:
- 提出一个新的医学知识增强快速学习 (MedKPL) 框架.
- 增强基于AI的临床笔记分类系统的性能和可转移性.
主要方法:
- 开发了MedKPL,将异构的知识来源 (知识图,QA数据库) 统一成固定格式的文本序列.
- 将统一的医疗知识集成到提示中,以便在AI模型中改进上下文表示.
- 评估了两个医疗数据集的框架,用于分类和跨部门转移任务.
主要成果:
- 与现有方法相比,MedKPL取得了优异的医学文本分类结果.
- 在零射击和零射击跨部门转移任务中表现出更好的表现.
- 该框架有效地整合了明确和隐含的医学知识.
结论:
- MedKPL提供了一个强大的解决方案,用于将各种医学知识集成到AI诊断系统中.
- 该框架提高了AI诊断系统的可解释性和可转移性,特别是在低数据场景中.
- MedKPL有可能显著推进人工智能驱动的临床决策支持.
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