第一部分:帕金森病中复杂实体和不平衡关系的关系三重提取模型
Xiaoming Zhang1, Can Yu1, Rui Yan2
1School of Information Science and Engineering, Hebei University of Science and Technology, Hebei 050018, China.
Journal of biomedical informatics
|March 11, 2024
概括
这项研究介绍了ParTRE,这是一种从帕金森病文本中提取医疗信息的新型模型. ParTRE准确地识别了复杂的关系,改善了医学知识图表的构建.
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.
- 生物医学数据科学 生物医学数据科学
背景情况:
- 从非结构化医学文本中精确的关系三重提取对于构建医学知识图表至关重要.
- 帕金森病由于复杂和重叠的实体而带来挑战,特别是有限的数据.
研究的目的:
- 在帕金森病文本中开发一个强大的关系三重提取模型.
- 解决医疗文本挖掘中实体重叠和不平衡关系类别的挑战.
主要方法:
- 一个基于标记的三阶段模型,ParTRE,是使用BiLSTM模块开发的,用于增强的上下文表示.
- 为了准确地从互补方向提取实体对,采用了条件规范化层.
- 基于焦点损失的适应性损失函数策略被推导出来处理不平衡的关系类别.
主要成果:
- 在新建的帕金森病病体上,ParTRE模型实现了93.3%的F1得分.
- 该模型在公共数据集上与最先进的方法显示了可比性能的表现.
- ParTRE成功地解决了重叠实体和不平衡关系类别的问题.
结论:
- 拟议的ParTRE模型为帕金森病的关系三重提取提供了有效和有效的方法.
- 这种方法可以集成到医学知识图表中,推进医学智能.
- 这项研究为帕金森病文本分析提供了专门的语料库和高性能模型.
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