Research on Medical Named Entity Identification Based On Prompt-Biomrc Model and Its Application in Intelligent Consultation System

TL;DR

This paper presents the Prompt-bioMRC model, a novel approach combining hard and soft prompts to improve medical named entity recognition, demonstrating superior accuracy and efficiency in medical data processing applications.

Contribution

The study introduces the Prompt-bioMRC model that integrates prompt learning with BioBERT for enhanced medical NER, advancing automated medical data extraction techniques.

Findings

Outperforms traditional NER models in medical datasets

Enhances accuracy and efficiency of medical entity recognition

Supports applications like intelligent diagnosis systems

Abstract

This study is dedicated to exploring the application of prompt learning methods to advance Named Entity Recognition (NER) within the medical domain. In recent years, the emergence of large-scale models has driven significant progress in NER tasks, particularly with the introduction of the BioBERT language model, which has greatly enhanced NER capabilities in medical texts. Our research introduces the Prompt-bioMRC model, which integrates both hard template and soft prompt designs aimed at refining the precision and efficiency of medical entity recognition. Through extensive experimentation across diverse medical datasets, our findings consistently demonstrate that our approach surpasses traditional models. This enhancement not only validates the efficacy of our methodology but also highlights its potential to provide reliable technological support for applications like intelligent diagnosis systems. By leveraging advanced NER techniques, this study contributes to advancing automated medical data processing, facilitating more accurate medical information extraction, and supporting efficient healthcare decision-making processes.