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Towards a Generalizable Time Expression Model for Temporal Reasoning in Clinical Notes.

Sumithra Velupillai1, Danielle L Mowery2, Samir Abdelrahman3

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Summary
This summary is machine-generated.

This study enhances temporal expression (TIMEX3) identification in clinical text. The developed approach achieves competitive results and informs the creation of a generalized temporal model for biomedical NLP tasks.

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Area of Science:

  • Natural Language Processing (NLP)
  • Clinical Informatics
  • Biomedical Data Science

Background:

  • Accurate temporal identification is crucial for clinical tasks like timeline generation and phenotyping.
  • Developing generalizable temporal modeling approaches across diverse clinical corpora remains a significant NLP challenge.

Purpose of the Study:

  • To generalize existing solutions for automated temporal expression (TIMEX3) span detection.
  • To assess similarities and differences in TIMEX3 models across separate clinical corpora.
  • To inform the development of a simplified, general temporal model for biomedical applications.

Main Methods:

  • Systematic approach to generalize automated TIMEX3 span detection.
  • Evaluation of various TIMEX3 model instantiations on distinct clinical corpora (2012 i2b2, 2015 Clinical TempEval).

Main Results:

  • Achieved competitive results for automated TIMEX3 classification.
  • Identified key similarities and differences in TIMEX3 modeling across corpora.

Conclusions:

  • The proposed approach is successful in generalizing TIMEX3 detection.
  • Findings provide valuable insights for developing a more simplified and generalizable temporal model for clinical NLP.