Temporal Relation Identification and Classification in Clinical Notes

  • Authors:

  • Jennifer D'Souza;Vincent Ng

  • Affiliations:

  • Human Language Technology Research Institute, University of Texas at Dallas, Richardson, TX 75083-0688;Human Language Technology Research Institute, University of Texas at Dallas, Richardson, TX 75083-0688

  • Venue:
  • Proceedings of the International Conference on Bioinformatics, Computational Biology and Biomedical Informatics
  • Year:
  • 2013

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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.