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对比医疗文本的少数镜头和传统命名实体识别模型

Yao Ge¹, Yuting Guo¹, Yuan-Chi Yang¹

¹Department of Biomedical Informatics School of Medicine, Emory University Atlanta, GA.

Proceedings. IEEE International Conference on Healthcare Informatics

|August 29, 2023

概括

少数射击学习 (FSL) 模型在医疗命名实体识别 (NER) 任务中扎,即使是小数据集. 目前的FSL和传统模型表现不佳,特别是在杂的医疗文本数据上,这表明需要进一步研究.

科学领域:

自然语言处理 (NLP) 是一种自然语言处理.
机器学习 (ML) 是指机器学习.
生物医学信息学生物医学信息学

背景情况:

监督机器学习,特别是深度神经网络,需要大量的注释数据集,这些数据集在医学研究中往往很少 (例如,罕见疾病).
短暂学习 (FSL) 旨在通过有限的注释数据来应对挑战.
缺乏在不同训练集大小中对FSL和医疗文本命名实体识别 (NER) 传统模型的性能比较.

研究的目的:

通过比较多个FSL模型与医学文本NER.传统模型的性能来弥合研究差距.
通过使用与健康相关的NER数据集,评估不同培训集大小的模型性能.

主要方法:

基准测试了三种传统的NER模型 (BERT-线性分类器,BERT-CRF,SANER) 和三种FSL NER模型 (StructShot & NNShot,Few-Shot Slot Tagging,ProtoNER).
使用了五个与健康相关的注释NER数据集进行实验.
在不同训练数据量中分析模型性能.

主要成果:

传统和FSL模型在小型医疗数据集上表现明显低于最先进的性能,F1得分通常低于30%.
与非医疗文本报告的性能相比,FSL模型在医疗文本上的表现不佳.
在杂的医学文本来源 (如社交媒体) 上,FSL方法的表现较差,而不是更清洁的来源 (如医学文献).

结论:

当前最先进的FSL系统还不适合在医学NLP中有效的NER.
需要进一步的研究,以提高FSL模型的性能,用于医学文本分析.
开发专门的,标准化的数据集,反映现实世界的场景,可以在这个领域推进FSL方法.

关键词:

有几次射击学习学习机器学习机器学习医疗信息学医疗信息学自然语言处理自然语言处理.文本挖掘 (Text Mining) 是一个很好的方法.

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