Named Entity Recognition in Online Medical Consultation Using Deep Learning

Named entity recognition in online medical consultation aims to address the challenge of identifying various types of medical entities within complex and unstructured social text in the context of online medical consultations. This can provide important data support for constructing more powerful online medical consultation knowledge graphs and improving virtual intelligent health assistants. A dataset of 26 medical entity types for named entity recognition for online medical consultations is first constructed. Then, a novel approach for deep named entity recognition in the medical field based on the fusion context mechanism is proposed. This approach captures enhanced local and global contextual semantic representations of online medical consultation text while simultaneously modeling high- and low-order feature interactions between local and global contexts, thereby effectively improving the sequence labeling performance. The experimental results show that the proposed approach can effectively identify 26 medical entity types with an average F1 score of 85.47%, outperforming the state-of-the-art (SOTA) method. The practical significance of this study lies in improving the efficiency and performance of domain-specific knowledge extraction in online medical consultation, supporting the development of virtual intelligent health assistants based on large language models and enabling real-time intelligent medical decision-making, thereby helping patients and their caregivers access common medical information more promptly.