Temporal Relation Identification and Classification in Clinical Notes

Abstract

We examine the task of temporal relation classification for the clinical domain. Our approach to this task departs from existing ones in that it is (1) knowledge-rich, employing sophisticated knowledge derived from semantic and discourse relations, and (2) hybrid, combining the strengths of rule-based and learning-based approaches. Evaluation results on the i2b2 Clinical Temporal Relations Challenge corpus show that our approach yields a 15--21% and 6--13% relative reduction in error over a state-of-the-art learning-based baseline system when gold-standard and automatically identified temporal relations are used, respectively.