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医疗实体链接使用关系诱导的密集检索

Ayush Singh¹, Saranya Krishnamoorthy¹, John E Ortega¹

¹inQbator AI, Evernorth Health Services, Saint Louis, MO USA.

Journal of healthcare informatics research

|April 29, 2024

概括

NeighBERT通过将知识图关系纳入变压器模型来增强临床文本分析. 这提高了电子健康记录上的医疗实体链接 (MEL) 和命名实体识别 (NER) 性能.

科学领域:

临床自然语言处理临床自然语言处理
医疗保健中的人工智能
生物信息学是一种生物信息学.

背景情况:

医疗实体链接 (MEL) 对于临床NLP至关重要,但受到电子健康记录 (EHR) 中模糊文本的挑战.
现有的变压器模型显示出希望,但与临床语言固有的模糊性作斗争.
解决模两可是提高从临床笔记中提取信息准确性的关键.

研究的目的:

引入NeighBERT,这是一种针对变压器模型的新型预训练技术,旨在解决临床文本中的模两可.
为了提高医疗实体链接和命名实体识别的性能.
改进从电子健康记录中提取相关的医疗信息.

主要方法:

开发了NeighBERT,这是一个定制的预训练技术,通过从知识图中编码实体关系来扩展BERT.
将关系上下文集成到变压器架构中,以更好地解决文本的模两可.
在两个标准临床数据集上评估NeighBERT,用于命名实体识别和医疗实体链接任务.

主要成果:

在命名实体识别 (NER) 精度,回忆和F1得分方面,NeighBERT显著提高了1-3分.
医疗实体链接 (MEL) 的表现取得了实质性增长,F1得分提高了10-15分.
关系上下文编码有效地减少了临床文本处理中的模两可.

结论:

NeighBERT提供了一种强大的新方法来增强基于变压器的临床NLP任务,特别是MEL和NER.
该方法成功地结合了关系知识,克服了标准BERT在处理模两可的临床文本方面的局限性.
这一进步有可能从电子健康记录中提取更准确,更可靠的信息.

关键词:

生物医学生物医学深度学习是一种深度学习.搜索和检索信息的搜索和检索.知识图表知识图表医疗实体链接医疗实体链接自然语言处理自然语言处理.

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