Fang Li1, Jingcheng Du1, Yongqun He2, Hsing-Yi Song1, Mohcine Madkour3, Guozheng Rao4, Yang Xiang1, Yi Luo1, Henry W Chen1,5, Sijia Liu6, Liwei Wang6, Hongfang Liu6, Hua Xu1, Cui Tao1. 1. School of Biomedical Informatics, University of Texas Health Science Center at Houston, Houston, Texas, USA. 2. Department of Computational Medicine and Bioinformatics, University of Michigan Medical School, Ann Arbor, Michigan, USA. 3. Advanced Analytics, Cummins Inc, Columbus, Indiana, USA. 4. College of Intelligence and Computing, Tianjin University, Tianjin, China. 5. University of Texas Southwestern Medical Center, Dallas, Texas, USA. 6. Department of Health Sciences Research, Mayo Clinic, Rochester, Minnesota, USA.
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.
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.
Authors: Sunghwan Sohn; Kavishwar B Wagholikar; Dingcheng Li; Siddhartha R Jonnalagadda; Cui Tao; Ravikumar Komandur Elayavilli; Hongfang Liu Journal: J Am Med Inform Assoc Date: 2013-04-04 Impact factor: 4.497
Authors: Jingcheng Du; Yang Xiang; Madhuri Sankaranarayanapillai; Meng Zhang; Jingqi Wang; Yuqi Si; Huy Anh Pham; Hua Xu; Yong Chen; Cui Tao Journal: J Am Med Inform Assoc Date: 2021-07-14 Impact factor: 4.497
Authors: Alison Callahan; Vladimir Polony; José D Posada; Juan M Banda; Saurabh Gombar; Nigam H Shah Journal: J Am Med Inform Assoc Date: 2021-07-14 Impact factor: 4.497