用提示学习提升医疗实体识别精度，助力智能问诊系统。
Research on Medical Named Entity Identification Based On Prompt-Biomrc Model and Its Application in Intelligent Consultation System
- 结合硬模板与软提示设计，优化医疗文本实体识别。
- 在多个医学数据集上优于传统模型，识别更精准高效。
- 适合医疗智能化、临床决策支持系统开发者参考。
本研究探索提示学习在医疗领域命名实体识别（NER）中的应用。近年来，大模型的发展推动了NER任务的进步，尤其是BioBERT语言模型显著提升了医学文本的识别能力。本文提出Prompt-bioMRC模型，融合硬模板与软提示设计，旨在提高医疗实体识别的精度与效率。在多个医学数据集上的实验表明，该方法持续优于传统模型。结果不仅验证了方法的有效性，也凸显其在智能诊断系统等应用场景中的技术支撑潜力。通过先进NER技术，本研究推动了医疗数据自动化处理，促进更准确的信息提取，支持高效医疗决策。
原文摘要 · Abstract (English)
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.
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