利用基于规则的NLP将文本报告翻译为由临床决策支持系统自动处理的结构化输入
Akram Redjdal1, Natallia Novikava1, Emmanuelle Kempf1,2
1Sorbonne Université, Université Sorbonne Paris Nord, INSERM, LIMICS, Paris, France.
Studies in health technology and informatics
|August 23, 2024
概括
一种新的基于规则的自然语言处理 (NLP) 方法有效地从报告中提取乳腺癌患者的数据. 这些结构化数据与临床决策支持系统 (CDSS) 集成,改善癌症管理.
科学领域:
- 计算语言学和医疗信息学.
- 自然语言处理 (NLP) 在瘤学中的应用.
背景情况:
- 临床决策支持系统 (CDSS) 需要结构化的患者数据,以有效管理乳腺癌.
- 从非结构化的临床报告中提取这些数据具有挑战性,当前的机器学习方法经常充当黑子.
研究的目的:
- 开发和评估一种基于规则的NLP方法,用于自动提取乳腺癌患者数据.
- 将非结构化的患者摘要转化为结构化的个人资料,以便在DESIREE项目中基于指导方针的CDSS输入.
主要方法:
- 实施基于规则的NLP管道,包括命名实体识别 (NER),关系提取和结构化数据提取.
- 将患者数据系统地组织成结构化个人资料.
- 从NLP提取的数据生成的CDSS建议与手工创建的患者资料 (黄金标准) 的比较.
主要成果:
- 基于规则的NLP方法与治疗建议有很强的一致性,与黄金标准相比,只有2%的差异.
- 该NER管道实现了高性能:主要实体 (患者,侧,瘤) 的平均F1分数为0.9.
- 关系提取实现了0.87的F1得分,而上下文信息提取实现了0.75.
结论:
- 基于规则的NLP提供了一种透明和有效的方法,用于从临床报告中对乳腺癌患者数据进行结构化.
- 开发的方法显示了将其整合到CDSS的有希望的结果,可能有助于改善乳腺癌管理.
- 高性能指标表明了临床文本分析的基于规则的NLP方法的稳定性.
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