Ma Xiaojun, Imai Takeshi, Shinohara Emiko, Sakurai Ryota, Kozaki Kouji, Ohe Kazuhiko
Graduate School of Medicine, The University of Tokyo, Tokyo, Japan.
The University of Tokyo Hospital, Tokyo, Japan.
Stud Health Technol Inform. 2017;245:910-914.
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.
疾病本体被定义为异常状态的因果链，被认为是医学信息系统中有价值的知识库。电子健康记录（EHR）与疾病本体之间的自动映射对于在实际临床环境中应用疾病本体至关重要。基于对148种慢性病本体的分析，约41%的异常状态需要从临床叙述中提取信息。本文提出了一个半自动框架来识别临床叙述中的异常状态。该框架旨在有效构建EHR与疾病本体之间的映射模块。我们表明，所提出的方法在慢性病本体中18%-33%的异常状态的数据映射中是有效的。此外，我们分析了我们的方法在从临床叙述中提取信息时无效的异常状态。
