Identifying direct temporal relations between time and events from clinical notes

• Published in: BMC medical informatics and decision making Vol. 18; no. Suppl 2; pp. 49 - 34
• Main Authors: Lee, Hee-Jin, Zhang, Yaoyun, Jiang, Min, Xu, Jun, Tao, Cui, Xu, Hua
• Format: Journal Article
• Language: English
• Published: England BioMed Central Ltd 23.07.2018; BioMed Central; BMC
• Subjects: Bioinformatics; Computational biology; Delivery of Health Care; Direct temporal relation; Documentation; Event history analysis; Humans; Identification; Information extraction; Information retrieval; Information Storage and Retrieval; Innovations; Management; Medical Records; Methods; Narratives; Natural language processing; Semantics; Syntactic structure; Target recognition; Temporal relation identification; Therapeutic applications; Time Factors; TLINK; Information extraction; Temporal relation identification; Direct temporal relation; Syntactic structure; TLINK
• ISSN: 1472-6947; 1472-6947
• DOI: 10.1186/s12911-018-0627-5

Most of the current work on clinical temporal relation identification follows the convention developed in the general domain, aiming to identify a comprehensive set of temporal relations from a document including both explicit and implicit relations. While such a comprehensive set can represent temporal information in a document in a complete manner, some of the temporal relations in the comprehensive set may not be essential depending on the clinical application of interest. Moreover, as the types of evidence that should be used to identify explicit and implicit relations are different, current clinical temporal relation identification systems that target both explicit and implicit relations still show low performances for practical use. In this paper, we propose to focus on a sub-task of conventional temporal relation identification task in order to provide insight into building practical temporal relation identification modules for clinical text. We focus on identification of direct temporal relations, a subset of temporal relations that is chosen to minimize the amount of inference required to identify the relations. A corpus on direct temporal relations between time expressions and event mentions is constructed, and an automatic system tailored for direct temporal relations is developed. It is shown that the direct temporal relations constitute a major category of temporal relations that contain important information needed for clinical applications. The system optimized for direct temporal relations achieves better performance than the state-of-the-art system developed with comprehensive set of both explicit and implicit relations in mind. We expect direct temporal relations to facilitate the development of practical temporal information extraction tools in clinical domain.