积极学习管道自动识别CDSS本体学的候选术语:测量,实验和性能
Shailesh Alluri1, Keerthana Komatineni1, Rohan Goli1
1Clemson University, Clemson, SC, USA.
概括
本研究引入了一个主动学习框架,用于自动化临床决策支持系统 (CDSS) 实体学的关键词识别. 这种方法减少了在本体论开发和维护中的手工工作.
科学领域:
- 医疗信息学 医疗信息学
- 医疗保健中的人工智能
- 对于本体论的自然语言处理.
背景情况:
- 临床决策支持系统 (CDSS) 实体学对于标准化医疗词汇和数据集成至关重要.
- 传统的本体论开发是手动的,耗时的,需要广泛的领域专业知识.
- 为CDSS本体学自动化关键词识别可以显著提高效率.
研究的目的:
- 呈现一个主动学习框架,用于自动识别与CDSS本体学相关的关键词.
- 为了减少本体论开发和长期维护所需的手工劳动和领域专业知识.
- 通过自动化方法提高CDSS本体结构创建的准确性和效率.
主要方法:
- 一个BiLSTM CRF模型集成到一个人-in-the-loop积极学习管道中.
- 文件选择的不确定性抽样策略,优先考虑人类审查的低可信度模型实例.
- 新的不确定性聚合方法 (KPSum,KPAvg,DOCSum,DOCAvg) 与不确定性措施 (MTP,TE,Margin) 结合,用于文档级别的信任评分.
主要成果:
- 拟议的积极学习框架有效地确定了CDSS本体学的关键短语.
- 新的不确定性聚合方法增强了主动学习中的文档选择过程.
- 这种方法导致了更透明,可复制和高效的文件优先级注释.
结论:
- 积极学习通过减少手工劳动,极大地促进了CDSS本体论的开发.
- 该框架支持人类专家,特别是在长期维护本体学方面.
- 自动化关键短语识别是有效和准确的本体学工程的一个有价值的策略.
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