摘要
以罕见病领域的研究为例,通过对Web of Science核心合集和MEDLINE数据库中的罕见病研究文献进行本体、人工智能和罕见病命名实体的识别和关系抽取,并构建实体间的耦合关系和共现关系网络,分析本体、人工智能及罕见病关联网络的特征,挖掘本体和人工智能在罕见病研究中的应用模式,并预测本体和人工智能技术未来可能的罕见病研究应用。结果表明:30%~40%的罕见病在研究中应用了本体或人工智能技术;基于本体和人工智能耦合关系形成的罕见病网络结构清晰,可划分为较为明显的罕见病集群。本体与人工智能技术协同应用于罕见病研究呈现出5种基本模式,基因本体、癫痫本体、机器学习、模式识别等本体以及人工智能在罕见病研究领域具有较大的应用潜力。
Taking rare disease research as an example,this study conducts recognition and relation extraction of ontology,artificial intelligence(AI),and rare disease named entity from rare disease research literature in the Web of Science Core Collection and MEDLINE databases.It constructs coupling and co-occurrence networks among entities,analyzes the characteristics of ontology,AI,and rare disease association networks,and explores the application patterns of ontologies and AI in rare disease research.Additionally,it predicts potential future applications of these technologies in the field.The results show that 30%to 40%of rare diseases have applied ontologies or AI technologies in research;the network structure of rare diseases formed based on the coupling relationship between ontologies and AI is clear and can be divided into distinct rare disease clusters.The collaborative application of ontologies and AI technologies in rare disease research presents 5 basic patterns,and ontologies such as gene ontology,epilepsy ontology,as well as AI technologies such as machine learning and pattern recognition,have great potential for application in the field of rare disease research.
作者
邰杨芳
魏宇虹
杨慧丽
郭樱
TAI Yangfang;WEI Yuhong;YANG Huili;GUO Ying(School of Management,Shanxi Medical University,Jinzhong 030600,China)
出处
《竞争情报》
2025年第2期48-58,共11页
Competitive Intelligence
基金
山西省高等学校一般性教学改革创新项目(编号:J20230538)
山西省研究生创新项目(编号:2023SJ169)
山西医科大学校级教育教学改革一般研究项目(编号:SXJ202079)的研究成果之一。
关键词
本体
人工智能
罕见病
社会网络分析
关联分析
链路预测
ontology
artificial intelligence
rare diseases
social network analysis
association analysis
link prediction
