Time event ontology (TEO): to support semantic representation and reasoning of complex temporal relations of clinical events

• Published in: Journal of the American Medical Informatics Association : JAMIA 2020-07, Vol.27 (7), p.1046-1056
• Main Authors: Li, Fang, Du, Jingcheng, He, Yongqun, Song, Hsing-Yi, Madkour, Mohcine, Rao, Guozheng, Xiang, Yang, Luo, Yi, Chen, Henry W, Liu, Sijia, Wang, Liwei, Liu, Hongfang, Xu, Hua, Tao, Cui
• Format: Article
• Language: English
• Subjects: Biological Ontologies; Decision Support Systems, Clinical; Electronic Health Records; Humans; Natural Language Processing; Research and Applications; Semantic Web; Time
• ISSN: 1527-974X; 1067-5027; 1527-974X
• DOI: 10.1093/jamia/ocaa058

Abstract Objective The goal of this study is to develop a robust Time Event Ontology (TEO), which can formally represent and reason both structured and unstructured temporal information. Materials and Methods Using our previous Clinical Narrative Temporal Relation Ontology 1.0 and 2.0 as a starting point, we redesigned concept primitives (clinical events and temporal expressions) and enriched temporal relations. Specifically, 2 sets of temporal relations (Allen’s interval algebra and a novel suite of basic time relations) were used to specify qualitative temporal order relations, and a Temporal Relation Statement was designed to formalize quantitative temporal relations. Moreover, a variety of data properties were defined to represent diversified temporal expressions in clinical narratives. Results TEO has a rich set of classes and properties (object, data, and annotation). When evaluated with real electronic health record data from the Mayo Clinic, it could faithfully represent more than 95% of the temporal expressions. Its reasoning ability was further demonstrated on a sample drug adverse event report annotated with respect to TEO. The results showed that our Java-based TEO reasoner could answer a set of frequently asked time-related queries, demonstrating that TEO has a strong capability of reasoning complex temporal relations. Conclusion TEO can support flexible temporal relation representation and reasoning. Our next step will be to apply TEO to the natural language processing field to facilitate automated temporal information annotation, extraction, and timeline reasoning to better support time-based clinical decision-making.