Research on Medical Text Classification Based on Improved Capsule Network

Abstract

In the medical field, text classification based on natural language process (NLP) has shown good results and has great practical application prospects such as clinical medical value, but most existing research focuses on English electronic medical record data, and there is less research on the natural language processing task for Chinese electronic medical records. Most of the current Chinese electronic medical records are non-institutionalized texts, which generally have low utilization rates and inconsistent terminology, often mingling patients’ symptoms, medications, diagnoses, and other essential information. In this paper, we propose a Capsule network model for electronic medical record classification, which combines LSTM and GRU models and relies on a unique routing structure to extract complex Chinese medical text features. The experimental results show that this model outperforms several other baseline models and achieves excellent results with an F1 value of 73.51% on the Chinese electronic medical record dataset, at least 4.1% better than other baseline models.