Research on Medical Named Entity Identification Based On Prompt-Biomrc Model and Its Application in Intelligent Consultation System
This work addresses medical NER to enhance automated data processing for healthcare decision-making, representing an incremental improvement over existing methods like BioBERT.
This study tackled medical Named Entity Recognition (NER) by introducing the Prompt-bioMRC model, which integrates hard template and soft prompt designs to improve precision and efficiency. The approach consistently surpassed traditional models across diverse medical datasets, demonstrating its effectiveness for applications like intelligent diagnosis systems.
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.