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Small and imbalanced datasets commonly seen in healthcare represent achallenge when training classifiers based on deep learning models. Somotivated, we propose a novel framework based on BioBERT (Bidirectional EncoderRepresentations from Transformers forBiomedical TextMining). Specifically, (i)we introduce Label Embeddings for Self-Attention in each layer of BERT, whichwe call LESA-BERT, and (ii) by distilling LESA-BERT to smaller variants, we aimto reduce overfitting and model size when working on small datasets. As anapplication, our framework is utilized to build a model for patient portalmessage triage that classifies the urgency of a message into three categories:non-urgent, medium and urgent. Experiments demonstrate that our approach canoutperform 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}.

Students Need More Attention: BERT-based AttentionModel for Small Data with Application to AutomaticPatient Message Triage