Towards temporal relation discovery from the clinical narrative.

Guergana Savova, Steven Bethard, Will Styler, James Martin, Martha Palmer, James Masanz, Wayne Ward

Research output: Contribution to journalArticlepeer-review

45 Scopus citations


Disease progression and understanding relies on temporal concepts. Discovery of automated temporal relations and timelines from the clinical narrative allows for mining large data sets of clinical text to uncover patterns at the disease and patient level. Our overall goal is the complex task of building a system for automated temporal relation discovery. As a first step, we evaluate enabling methods from the general natural language processing domain - deep parsing and semantic role labeling in predicate-argument structures - to explore their portability to the clinical domain. As a second step, we develop an annotation schema for temporal relations based on TimeML. In this paper we report results and findings from these first steps. Our next efforts will scale up the data collection to develop domain-specific modules for the enabling technologies within Mayo's open-source clinical Text Analysis and Knowledge Extraction System.

Original languageEnglish (US)
Pages (from-to)568-572
Number of pages5
JournalAMIA ... Annual Symposium proceedings / AMIA Symposium. AMIA Symposium
StatePublished - 2009

ASJC Scopus subject areas

  • Medicine(all)


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