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Identifying direct temporal relations between time and events from clinical notes.

Hee-Jin Lee1, Yaoyun Zhang1, Min Jiang2

  • 1School of Biomedical Informatics, The University of Texas Health Science Center at Houston, Houston, TX, USA.

BMC Medical Informatics and Decision Making
|August 2, 2018
PubMed
Summary
This summary is machine-generated.

This study introduces direct temporal relations for clinical text analysis, simplifying information extraction. This focused approach improves performance over comprehensive methods, aiding practical clinical applications.

Keywords:
Direct temporal relationInformation extractionSyntactic structureTLINKTemporal relation identification

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Area of Science:

  • Natural Language Processing
  • Clinical Informatics
  • Biomedical Text Mining

Background:

  • Current clinical temporal relation identification aims for comprehensive relation sets, including explicit and implicit types.
  • Comprehensive sets may include non-essential relations for specific clinical applications.
  • Existing systems struggle with low performance due to differing evidence for explicit and implicit relation identification.

Purpose of the Study:

  • To propose a focused sub-task for practical clinical temporal relation identification.
  • To introduce the concept and identification of direct temporal relations.
  • To develop an automatic system tailored for direct temporal relations.

Main Methods:

  • Focused on identifying direct temporal relations, a subset minimizing inference.
  • Constructed a corpus specifically for direct temporal relations between time expressions and event mentions.
  • Developed and optimized an automatic system for direct temporal relation identification.

Main Results:

  • Direct temporal relations represent a major category of clinically relevant temporal information.
  • The system optimized for direct temporal relations outperformed state-of-the-art systems using comprehensive relation sets.
  • Demonstrated improved performance compared to systems identifying both explicit and implicit relations.

Conclusions:

  • Direct temporal relations are crucial for clinical applications.
  • The developed system shows practical utility for temporal information extraction.
  • This focused approach facilitates the development of practical clinical information extraction tools.