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使用自然语言处理对标准化医学术语的机器翻译:一个范围审查

Richard Noll¹, Lena S Frischen², Martin Boeker³

¹Goethe University Frankfurt, University Hospital Frankfurt, Institute of Medical Informatics, Frankfurt, Germany.

New biotechnology

|August 31, 2023

概括

像SNOMED CT这样的医学术语的自动翻译从英语到德语对于全球研究至关重要. 神经机器翻译显示了在这个领域提高准确性的前景.

科学领域:

医疗信息学医疗信息学
计算语言学计算语言学
医疗保健服务研究医疗服务研究

背景情况:

标准化医疗术语对于准确的数据交换和通信至关重要.
许多重要的医学术语主要用英语提供,这为国际研究和自动化数据处理创造了障碍.
自然语言处理 (NLP) 和机器翻译 (MT) 为克服医疗数据中的语言障碍提供了潜在的解决方案.

研究的目的:

进行对标准化医学术语的自动翻译研究的范围审查.
识别不同语言翻译医疗术语的趋势,方法和挑战.
评估医疗领域的自动翻译工具和技术的现状.

主要方法:

在PubMed和Web of Science进行了全面的文献搜索.
搜索结果在标题,摘要和全文层面上被选.
提取的数据包括术语,语言,翻译类型,NLP/MT技术,系统,数据源和翻译质量指标.

主要成果:

最常被翻译的术语是SNOMED CT (39.1%),其次是MeSH和ICD (分别为13%).
英语是主要的源语言 (55.9%),德语是最常见的目标语言 (41.2%).
统计机器翻译 (SMT) 和神经机器翻译 (NMT) 是常见的方法,经常被谷歌翻译等商业工具补充,并使用BLEU等指标验证.

结论:

医学术语的自动翻译是一个活跃的研究领域,主要关注SNOMED CT.
趋势正在转向更先进的NMT方法,表明向更高的翻译质量迈进.
需要进一步的研究来优化翻译的准确性,并确保在全球卫生倡议中可靠地使用翻译的医学术语.

关键词:

控制的词汇管理管理词汇管理机器翻译机器翻译在NLP中,我们使用了NLP.

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