在疾病关系提取图表上的多式学习
Yucong Lin1, Keming Lu2, Sheng Yu3
1Institute of Engineering Medicine, Beijing Institute of Technology, Beijing, China; Beijing Engineering Research Center of Mixed Reality and Advanced Display, School of Optics and Photonics, Beijing Institute of Technology, Beijing, China.
作为一种多式联络方法,REMAP通过将不完整的知识图与医疗文本相融合来增强疾病关系提取. 这种方法提高了准确性和F1分数,使得更好地发现疾病联系.
科学领域:
- 人工智能的人工智能
- 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.
背景情况:
- 疾病知识图表对于组织复杂的疾病信息至关重要.
- 由于文本中的分散数据和不完整的图形,提取准确的疾病关系是具有挑战性的.
- 为了构建全面的知识图表,需要多模式数据融合.
研究的目的:
- 引入REMAP,一种用于疾病关系提取的新型多式联络方法.
- 通过整合各种数据源来提高疾病知识图的准确性和完整性.
- 为了实现强大的关系提取,即使缺少数据模式.
主要方法:
- 雷马普将不完整的知识图和医学文本数据集联合嵌入到潜伏向量空间中.
- 一个脱的模型结构允许单模推理,解决缺失的数据场景.
- 该方法应用于一个大规模的疾病知识图表和文本库.
主要成果:
- REMAP提高了基于语言的疾病关系提取,精度为10.0%,F1得分为17.2%.
- 该方法在准确度方面超过了以图表为基础的方法8.4%,在推新的关系方面超过了F1得分的10.4%.
- 知识图和语言数据的融合显著增强了关系提取.
结论:
- 雷马普为疾病关系提取提供了一种灵活有效的多式联运战略.
- 该方法成功地将结构化知识与非结构化文本数据集成在一起.
- 这种模型有助于发现,访问和评估疾病概念关系.
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