A Semi-Automatic Framework to Identify Abnormal States in EHR Narratives
Disease ontology,defined as a causal chain of abnormal states,is believed to be a valuable knowledge base in medical information systems.Automatic mapping between electronic health records(EHR)and disease ontology is indispensable for applying disease ontology in real clinical settings.Based on an analysis of ontologies of 148 chronic diseases,approximately 41%of abnormal states require information extraction from clinical narratives.This paper presents a semi-automatic framework to identify abnormal states in clinical narratives.This framework aims to effectively build mapping modules between EHR and disease ontology.We show that the proposed method is effective in data mapping for 18%-33%of the abnormal states in the ontologies of chronic diseases.Moreover,we analyze the abnormal states for which our method is invalid in extracting information from clinical narratives.
Ontology Machine learning Natural language processing
Xiaojun Ma Takeshi Imai Emiko Shinohara Ryota Sakurai Kouji Kozaki Kazuhiko Ohe
Graduate School of Medicine,The University of Tokyo,Tokyo,Japan The University of Tokyo Hospital,Tokyo,Japan The Institute of Scientific and Industrial Research,Osaka University,Osaka,Japan Graduate School of Medicine,The University of Tokyo,Tokyo,Japan;The University of Tokyo Hospital,Tok
国际会议
第十六届世界医药健康信息学大会((MEDINFO2017)、第二届世界医药健康信息学华语论坛(WCHIS 2017)、第15届全国医药信息学大会(CMIA 2017)
苏州
英文
910-914
2017-08-21（万方平台首次上网日期，不代表论文的发表时间）