Abstract
Despite recent advances in the application of deep learning algorithms to various kinds of medical data, clinical text classification, and extracting information from narrative clinical notes remains a challenging task. The challenges of representing, training and interpreting document classification models are amplified when dealing with small and clinical domain data sets. The objective of this research is to investigate the attention-based deep learning models to classify the de-identified clinical progress notes extracted from a real-world EHR system. The attention-based deep learning models can be used to interpret the models and understand the critical words that drive the correct or incorrect classification of the clinical progress notes. The attention-based models in this research are capable of presenting the human interpretable text classification models. The results show that the fine-tuned BERT with the attention layer can achieve a high classification accuracy of 97.6%, which is higher than the baseline fine-tuned BERT classification model. Furthermore, we demonstrate that the attention-based models can identify relevant keywords that strongly relate to the corresponding clinical categories.
Abstract (translated)
URL
https://arxiv.org/abs/1910.05786
