多模式学习用于从临床文本中提取时间关系
1Faculty of Computer and Information Science, University of Ljubljana, Ljubljana 1000, Slovenia.
概括
本研究引入了一种双模架构,用于改善医疗文档中的时间关系提取. 通过整合文本和知识图表,它增强了对患者叙述和临床数据的理解.
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.
- 知识表示 知识表示
背景情况:
- 时间关系提取对于理解医疗文件中的患者叙述至关重要.
- 现有的方法往往仅依赖于文字信息,限制了它们的范围.
- 准确的时间理解对于临床决策和研究至关重要.
研究的目的:
- 开发和评估用于医学文本中时间关系提取的创新双模架构.
- 增强医学领域内叙事过程的理解.
- 改进从广泛的患者报告和笔记中提取时间关系.
主要方法:
- 开发了一个双模架构,集成文本文档和知识图的信息.
- 在时间关系提取过程中注入了关于事件的常识知识.
- 在模拟现实场景的各种临床数据集上进行了严格的测试.
主要成果:
- 拟议的双模架构与仅使用文本的方法相比,显示出更高的性能.
- 在多个临床数据集中评估有效性.
- 即使没有额外的上下文信息,也展示了强大的性能.
结论:
- 双模架构代表了时间关系提取的重大进步.
- 将文本数据与知识图集集成,可以提高对医学叙述的理解.
- 这种方法有望改善对患者旅程的理解和复杂医疗数据中的时间关系提取.
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