Miller Timothy, Bethard Steven, Dligach Dmitriy, Savova Guergana
Computational Health Informatics Program, Boston Children's Hospital, Harvard Medical School.
School of Information, University of Arizona.
Proc Conf Assoc Comput Linguist Meet. 2023 Jul;2023:313-319.
Understanding temporal relationships in text from electronic health records can be valuable for many important downstream clinical applications. Since Clinical TempEval 2017, there has been little work on end-to-end systems for temporal relation extraction, with most work focused on the setting where gold standard events and time expressions are given. In this work, we make use of a novel multi-headed attention mechanism on top of a pre-trained transformer encoder to allow the learning process to attend to multiple aspects of the contextualized embeddings. Our system achieves state of the art results on the THYME corpus by a wide margin, in both the in-domain and cross-domain settings.
理解电子健康记录文本中的时间关系对于许多重要的下游临床应用具有重要价值。自2017年临床时间评估（Clinical TempEval）以来，针对时间关系提取的端到端系统的研究工作较少，大多数工作集中在给定金标准事件和时间表达式的情况下。在这项工作中，我们在预训练的Transformer编码器之上使用了一种新颖的多头注意力机制，以使学习过程能够关注上下文嵌入的多个方面。我们的系统在THYME语料库的领域内和跨领域设置中均大幅领先，取得了最优的结果。
