MedTime: A temporal information extraction system for clinical narratives

Yu Kai Lin, Hsinchun Chen, Randall A. Brown

Research output: Contribution to journalArticlepeer-review

39 Scopus citations


Temporal information extraction from clinical narratives is of critical importance to many clinical applications. We participated in the EVENT/TIMEX3 track of the 2012 i2b2 clinical temporal relations challenge, and presented our temporal information extraction system, MedTime. MedTime comprises a cascade of rule-based and machine-learning pattern recognition procedures. It achieved a micro-averaged f-measure of 0.88 in both the recognitions of clinical events and temporal expressions. We proposed and evaluated three time normalization strategies to normalize relative time expressions in clinical texts. The accuracy was 0.68 in normalizing temporal expressions of dates, times, durations, and frequencies. This study demonstrates and evaluates the integration of rule-based and machine-learning-based approaches for high performance temporal information extraction from clinical narratives.

Original languageEnglish (US)
Pages (from-to)S20-S28
JournalJournal of Biomedical Informatics
Issue numberSUPPL.
StatePublished - Dec 2013


  • Event recognition
  • I2b2
  • Temporal expression recognition and normalization
  • Temporal information extraction

ASJC Scopus subject areas

  • Computer Science Applications
  • Health Informatics


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