计算机工程与应用2024，Vol.60Issue(24)：65-78,14.DOI:10.3778/j.issn.1002-8331.2403-0457
基于深度学习的医学实体和关系联合抽取研究综述
Review of Joint Extraction of Medical Entities and Relationships Based on Deep Learning
摘要
Abstract
Named entity recognition and relationship extraction are core tasks in the field of medical information extrac-tion.They enable the automatic identification of entities,entity types,and relationships between entities from unstructured or semi-structured text.This capability not only facilitates the discovery and integration of knowledge,application in clini-cal decision-making,and enhancement of drug discovery and repurposing,but also supports public health monitoring and disease prevention.This article begins by reviewing the development of entity recognition and relationship extraction,introducing common evaluation metrics and datasets for joint entity and relationship extraction in the medical field.It highlights current challenges in the field,such as the complexity of medical text structures and the low accuracy of joint extraction.Building on these issues,the article further explores the application of deep learning-based methods for joint entity and relationship extraction in the medical field.These methods are primarily categorized into joint extraction models based on shared parameters and those based on joint decoding.The article discusses and summarizes the advantages and disadvantages of different models from a problem-solving perspective.Finally,the article discusses the challenges in joint entity and relationship extraction within the medical field and suggests future research directions.关键词
医学文本/联合抽取/关系抽取/实体识别Key words
medical text/joint extraction of entity and relation/relation extraction/named entity recognition分类
信息技术与安全科学引用本文复制引用
叶青,张晓凤,彭琳,程春雷..基于深度学习的医学实体和关系联合抽取研究综述[J].计算机工程与应用,2024,60(24):65-78,14.基金项目
国家自然科学基金(82260988) （82260988）
江西省自然科学基金(20224BAB206102). （20224BAB206102）
