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Multimodal learning for temporal relation extraction in clinical texts.

Timotej Knez1, Slavko Žitnik1

  • 1Faculty of Computer and Information Science, University of Ljubljana, Ljubljana 1000, Slovenia.

Journal of the American Medical Informatics Association : JAMIA
|March 26, 2024
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This study introduces a bimodal architecture for improved temporal relation extraction in medical documents. By integrating text and knowledge graphs, it enhances understanding of patient narratives and clinical data.

Keywords:
knowledge graphsnatural language processingtemporal relation extractiontransformer-architecture

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Area of Science:

  • Medical Informatics
  • Natural Language Processing
  • Knowledge Representation

Background:

  • Temporal relation extraction is crucial for understanding patient narratives in medical documents.
  • Existing methods often rely solely on textual information, limiting their scope.
  • Accurate temporal understanding is vital for clinical decision-making and research.

Purpose of the Study:

  • To develop and evaluate an innovative bimodal architecture for temporal relation extraction in medical texts.
  • To enhance the understanding of narrative processes within the medical domain.
  • To improve the extraction of temporal relationships from extensive patient reports and notes.

Main Methods:

  • Developed a bimodal architecture integrating information from text documents and knowledge graphs.
  • Infused common-sense knowledge about events into the temporal relation extraction process.
  • Conducted rigorous testing on diverse clinical datasets simulating real-world scenarios.

Main Results:

  • The proposed bimodal architecture demonstrated superior performance compared to text-only methods.
  • Evaluated effectiveness across multiple clinical datasets.
  • Showcased robust performance even without additional contextual information.

Conclusions:

  • The bimodal architecture represents a significant advancement in temporal relation extraction.
  • Integrating textual data with knowledge graphs enhances understanding of medical narratives.
  • This approach promises to improve comprehension of patient journeys and temporal relationship extraction in complex medical data.