Graph-Augmented Cyclic Learning Framework for Similarity Estimation of
Medical Clinical Notes
- URL: http://arxiv.org/abs/2208.09437v1
- Date: Fri, 19 Aug 2022 16:34:41 GMT
- Title: Graph-Augmented Cyclic Learning Framework for Similarity Estimation of
Medical Clinical Notes
- Authors: Can Zheng, Yanshan Wang, Xiaowei Jia
- Abstract summary: 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.
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.
- Score: 6.891426146645747
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: 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.
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