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使用自然语言处理医疗自由文本的临床命名实体识别和关系提取:系统性审查

David Fraile Navarro¹, Kiran Ijaz¹, Dana Rezazadegan²

¹Centre for Health Informatics, Australian Institute of Health Innovation, Macquarie University, Sydney, Australia.

International journal of medical informatics

|June 9, 2023

概括

临床文本的自然语言处理 (NLP) 最近的进展显示了机器学习模型的强大性能. 然而,这些NLP工具需要有限的现实应用和强大的临床评估.

科学领域:

临床信息学临床信息学
自然语言处理自然语言处理.
机器学习机器学习

背景情况:

自然语言处理 (NLP) 应用于临床自由文本的应用已经迅速发展,特别是在命名实体识别和关系提取方面.
尽管取得了重大进展,但缺乏全面的概述和关于将这些NLP进步转化为临床实践的清晰度.

结论:

机器学习,特别是基于变压器的模型,主导临床信息提取的NLP,表现出高性能.
这些NLP发展的通用性和转化到临床实践仍然值得怀疑,因为依赖有限的数据集和缺乏强大的临床评估.

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