使用最小标记数据与层次上下文的关键词识别和转移学习
Rohan Goli1, Keerthana Komatineni1, Shailesh Alluri1
1School of Computing, College of Engineering, Computing and Applied Science, Clemson University, Clemson, SC, USA.
medRxiv : the preprint server for health sciences
|June 9, 2023
概括
本研究引入了一种半监督的框架,用于识别临床决策支持系统 (CDSS) 中的关键词 (KP),使用最小的标记数据. 这种新的方法通过自动化本体结构构建来提高医疗信息技术的互操作性.
科学领域:
- 卫生信息技术 信息技术
- 自然语言处理自然语言处理.
- 机器学习 机器学习
背景情况:
- 可互操作的临床决策支持系统 (CDSS) 规则对于医疗信息技术至关重要,但实现互操作性是一项挑战.
- 对互操作的CDSS规则至关重要的本体结构传统上依赖于领域专家的手动关键词 (KP) 识别.
- 自然语言处理 (NLP) 技术可以自动化KP识别,补充手工工作,但需要人类专业知识来标记数据.
研究的目的:
- 为CDSS子域提供一个半监督的关键词识别框架.
- 解决临床NLP任务中有限的人类标记数据的挑战.
- 为了提高互操作CDSS规则的实体结构的效率和准确性.
主要方法:
- 使用BiLSTM-CRF模型开发了一个半监督框架,具有层次关注和域调整.
- 在初始培训中使用合成标签,并使用最小的人类标签数据进行微调.
- 优化了NLP预处理和机器学习管道,评估不同的编码方案和上下文学习策略.
主要成果:
- 拟议的框架通过有效利用合成标签和文档层次上下文,优于先前的神经架构.
- 域调整技术改善了合成标签的质量,BIO编码方案显示出稍微更好的性能.
- 整合文档级上下文,预训练的语言模型和词嵌入,显著提高了模型性能.
结论:
- 这是CDSS子域中用于KP识别的第一个功能框架,基于有限的人类标记数据进行训练.
- 该框架通过提供轻量级的深度学习方法来实时识别KP,为临床NLP做出贡献.
- 这种自动化方法补充了人类专家,解决了在专业领域手动数据标签的挑战.
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