论文标题
图形增强的循环学习框架，以估算医学临床笔记的相似性
Graph-Augmented Cyclic Learning Framework for Similarity Estimation of Medical Clinical Notes
论文作者
论文摘要
临床领域中的语义文本相似性（STS）有助于提高诊断效率，并为下游数据挖掘任务提供简洁的文本。但是，鉴于诊所文本中涉及的高度领域知识，通用语言模型正确地推断出临床句子和输出相似性后的隐性医学关系仍然具有挑战性。在本文中，我们提出了一个图形的循环学习框架，用于临床领域中的相似性估计。该框架可以方便地在最先进的骨干语言模型上实现，并通过与基于辅助图的网络（GCN）网络共同培训来利用域知识来提高其性能。我们报告了通过将生物临床BERT基线分别提高16.3％和27.9％，在GCN和共同训练框架中引入领域知识的成功。
Semantic textual similarity (STS) in the clinical domain helps improve diagnostic efficiency and produce concise texts for downstream data mining tasks. However, given the high degree of domain knowledge involved in clinic text, it remains challenging for general language models to infer implicit medical relationships behind clinical sentences and output similarities correctly. In this paper, we present a graph-augmented cyclic learning framework for similarity estimation in the clinical domain. The framework can be conveniently implemented on a state-of-art backbone language model, and improve its performance by leveraging domain knowledge through co-training with an auxiliary graph convolution network (GCN) based network. We report the success of introducing domain knowledge in GCN and the co-training framework by improving the Bio-clinical BERT baseline by 16.3% and 27.9%, respectively.
