在SNOMED CT实体链接挑战中,SNOMED CT实体链接挑战
Rory Davidson1, Will Hardman2, Guy Amit3
1SNOMED International, London W2 6BD, United Kingdom.
概括
开发临床实体链接模型对于罕见的概念来说是具有挑战性的. 性能受到培训数据中的概念频率,和注释模两可的影响.
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.
背景情况:
- 临床实体链接对于组织电子健康记录至关重要.
- 标准化使用SNOMED CT等术语的临床概念对于互操作性至关重要.
研究的目的:
- 用MIMIC-IV-Note数据评估临床实体链接的不同方法.
- 确定影响实体链接模型表现的因素.
主要方法:
- 通过使用注释MIMIC-IV-Note数据和SNOMED CT.的子集进行了竞赛.
- 在272份核准说明中,有74,808份注释得到了整理.
- 提交的内容在字符级别使用平均交叉点-整个联盟指标进行了评估.
主要成果:
- 获胜的解决方案采用了不同的方法:基于字典,基于编码器和基于解码器.
- 训练数据中的概念频率显著影响了模型性能.
- 罕见的概念,高的概念和注释模糊性与较低的性能有关.
结论:
- 未来的实体链接研究应该优先考虑罕见的概念.
- 建议开发方法来处理稀缺的培训数据并利用上下文信息.
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