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使用交叉编码器和SNOMED CT CT进行吸烟状态规范化

Akhila Abdulnazar¹, Stefan Schulz¹, Markus Kreuzthaler¹

¹Institute for Medical Informatics, Statistics and Documentation, Medical University of Graz, Austria.

Studies in health technology and informatics

|May 17, 2025

概括

这项研究使用双编码器和交叉编码器模型标准化了德国临床笔记中的吸烟状况文档. 该方法在将叙事术语映射到SNOMED CT代码中实现了85%的准确性,以获得更好的医疗保健数据一致性.

科学领域:

医疗信息学医疗信息学
自然语言处理自然语言处理.
临床文档临床文档

背景情况:

准确的吸烟状态文件对于患者护理和临床决策至关重要.
吸烟状态经常嵌入非结构化的临床叙述中.
标准化临床数据提高了医疗保健系统的互操作性和一致性.

研究的目的:

为了使德国临床叙述中的吸烟状态提及正常化.
将叙事表达式映射到标准化的 SNOMED CT 代码.
提高电子健康记录中吸烟状态数据的一致性和可用性.

主要方法:

使用双编码器和交叉编码器重新排名模型进行自然语言处理.
开发了一个系统来识别和规范临床文本中的吸烟相关术语.
赋予SNOMED CT代码以标准化识别的吸烟状态信息.

主要成果:

在将叙事表达式映射到SNOMED CT定义中,实现了Recall@1的85%准确性.
从非结构化的临床叙述中成功标准化吸烟状态信息.
证明了该模型对德语临床数据的有效性.

结论:

双编码器和交叉编码器模型有效地将从临床叙述中标准化吸烟状态文档.
SNOMED CT编码增强了吸烟状态数据的一致性和临床实用性.
这种方法为改善临床文档质量提供了一个可扩展的解决方案.

关键词:

医学概念规范化医学概念规范化这就是SNOMED CT.吸烟状态吸烟状态

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