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Comparing Large Language Models for Improved Clinical Temporal Relation Extraction

  • By

  • Jianping He

  • Zenan Sun

  • Laila Rasmy

  • Haifang Li

  • Jianfu Li

  • Evan Yu

  • Degui Zhi

  • Cui Tao

  • October 15, 2026

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  • 1

    Clinical temporal relation extraction identifies temporal information about patients’ past, present, and future conditions that is not available in structured electronic health records.

  • 2

    Clinical temporal relation extraction reconstructs the chronology of events documented in clinical records.

  • 3

    Flexible note formats, atypical grammar, abbreviations, and acronyms make clinical temporal relation annotation difficult.

  • 4

    Clinical temporal relation extraction has lower interannotator agreement than clinical event and temporal mention annotation.

  • 5

    Clinical temporal relation extraction methods include rule-based, machine learning, deep learning, and hybrid approaches.

Original Source(s)

Optimizing Clinical Temporal Relation Extraction With Large Language Models: Comparative Analysis

  • by Jianping He, Zenan Sun, Laila Rasmy, Haifang Li, Jianfu Li, Evan Yu, Degui Zhi, Cui Tao

  • October 15, 2026

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