Entity-enhanced BERT for medical specialty prediction based on clinical questionnaire data.

ABSTRACT: A medical specialty prediction system for remote diagnosis can reduce the unexpected costs incurred by first-visit patients who visit the wrong hospital department for their symptoms. To develop medical specialty prediction systems, several researchers have explored clinical predictive models using real medical text data. Medical text data include large amounts of information regarding patients, which increases the sequence length. Hence, a few studies have attempted to extract entities from the text as concise features and provide domain-specific knowledge for clinical text classification. However, it is still insufficient to inject them into the model effectively. Thus, we propose Entity-enhanced BERT (E-BERT), which utilizes the structural attributes of BERT for medical specialty predic

SUBMITTER: Lee S 

PROVIDER: S-EPMC11781728 | biostudies-literature | 2025

REPOSITORIES: biostudies-literature