Medical chief complaint classification with hierarchical structure of label descriptions.

With rapid growth of online healthcare systems, chief complaint classification plays an important role in areas such as triage or doctor recommendation. Existing medical text classification techniques such as rule-based or learning-based methods fail to effectively utilize the inherent hierarchical structure of label descriptions that contain strong domain knowledge. In this paper, we propose a novel text classification framework for chief complaint by embedding both input text and hierarchical structure of label descriptions based on deep neural networks. The proposed framework makes use of not only three branches (i.e. chief complaint branch, main-category branch, and sub-category branch) with a Sequence Information Encoder to encode semantics from chief complaint and hierarchical structure of label descriptions but also a Hierarchical Relational Network with Attention module to capture complex relationships among them focusing on informative words with attentional scores. We evaluate our framework on two public medical datasets with label descriptions extracted from medical books and websites. Experimental results show that the proposed method outperforms other baseline techniques by a significant margin. The source code of our framework is available at ANONYMISED. [Display omitted] • Novel chief complaint classification model using hierarchical label descriptions. • SIE effectively embeds input text to capture sequential information with context. • HRNA captures relationships among chief complaint and category descriptions. • The framework greatly outperforms SoTA methods on two real world datasets. [ABSTRACT FROM AUTHOR]