一个开源的数据库和自动工具,用于西班牙医疗记录中的部分识别
Iker de la Iglesia1, María Vivó2, Paula Chocrón2
1HiTZ Basque Center for Language Technology Faculty of Engineering Bilbao University of the Basque Country (UPV/EHU), Spain(1).
本研究介绍了一套西班牙开源数据集,用于构建和评估电子临床叙述 (ECN) 的自动部分识别系统. 开发的B2度量和微调的语言模型提高了系统性能,即使在数据稀缺的情况下.
科学领域:
- 自然语言处理自然语言处理.
- 医疗信息学 医疗信息学
- 计算语言学 计算语言学
背景情况:
- 电子临床叙述 (ECN) 包含重要的健康信息,但缺乏足够的开源数据.
- 从结构化标题到非结构化注释,ECN的结构异质性阻碍了自动系统的开发和评估.
- 开发ECN的自动化系统对于有效提取和利用患者数据至关重要.
研究的目的:
- 提供西班牙的开源数据集,用于开发和评估ECN的自动区块识别系统.
- 设计和实施一个针对临床叙事部分识别的新型评估指标 (B2).
- 为了创建一个微调的语言模型,优化这个特定的任务.
主要方法:
- 标注西班牙临床进展记录的语料库,分为七个主要的部分类型.
- 评估现有指标,并定义新的B2指标,以改善特定任务的评估.
- 开发和发布了一个基线语言模型,为部分识别进行了微调.
主要成果:
- 开源注释的语料库,评估脚本和基线模型是公开的.
- 基线模型在开源数据集上获得了平均B2评分71.3.
- 该模型在数据稀缺场景中表现出能力,平均B2为67.0.0.
结论:
- 在非结构化的临床叙述中,自动区块识别在适当的数据和评估指标下是可行的.
- 这一贡献旨在加速开发更强大,更准确的处理ECN系统.
- 这些开源资源将促进临床文本分析领域的进一步研究和创新.
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