Small and imbalanced datasets commonly seen in healthcare represent a challenge when training classifiers based on deep learning models. So motivated, we propose a novel framework based on BioBERT (Bidirectional Encoder Representations from Transformers forBiomedical TextMining). Specifically, (i) we introduce Label Embeddings for Self-Attention in each layer of BERT, which we call LESA-BERT, and (ii) by distilling LESA-BERT to smaller variants, we aim to reduce overfitting and model size when working on small datasets. As an application, our framework is utilized to build a model for patient portal message triage that classifies the urgency of a message into three categories: non-urgent, medium and urgent. Experiments demonstrate that our approach can outperform several strong baseline classifiers by a significant margin of 4.3% in terms of macro F1 score. The code for this project is publicly available at \url{https://github.com/shijing001/text_classifiers}.

本研究提出了一种基于BioBERT的新框架，其中引入了一种用于自我关注的标签嵌入技术以及使用较小变体对BERT进行精简以减少过拟合和模型大小，应用于患者信息分类，证明了在处理医疗保健领域中的小数据集时，与传统分类器相比，能够实现更高的分类性能 .

BERT-based小数据关注模型及其应用于自动患者信息分类