在炎症性肠病的低资源环境下,临床实体意识的领域适应
Sumam Francis1, Fernando Crema Garcia2, Kanimozhi Uma1
1Language Intelligence and Information Retrieval (LIIR) Lab, Department of Computer Science, KU Leuven, Leuven, Belgium.
我们开发了一种新的工具,可以使用自然语言处理 (NLP) 来从电子健康记录中提取信息. 该系统增强了临床概念的提取,特别是在炎症性肠病 (IBD) 研究中.
科学领域:
- 生物医学信息学 生物医学信息学
- 自然语言处理自然语言处理.
- 临床数据挖掘 临床数据挖掘
背景情况:
- 电子健康记录 (EHR) 包含大量的非结构化临床文本.
- 从文本中提取有价值的信息是具有挑战性的,需要自动化工具.
- 生物医学实体提取对于医学研究和患者护理至关重要.
研究的目的:
- 开发一个强大的生物医学实体提取工具,用于炎症性肠病 (IBD) 数据.
- 调整一个通用语言模型,以便有效地提取IBD相关信息.
- 为临床荷兰命名实体识别 (NER) 创建一个全面的管道.
主要方法:
- 采用了特定领域的预培训和实体意识的掩盖策略.
- 使用对比学习来微调通用语言模型.
- 开发了一个端到端的NER工具,具有注释,校正和可视化功能.
主要成果:
- 成功开发了一种新的临床荷兰NER管道.
- 该方法证明了有效的域调整用于IBD信息提取.
- 开发的NER工具简化了医疗记录策划和临床工作流程.
结论:
- 这项研究在自动化临床文本分析方面取得了重大进展.
- 开发的工具增强了从非结构化EHR数据中检索关键的临床概念.
- 这项工作支持改善IBD的医疗记录策划和临床决策.
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