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Typed Markers and Context for Clinical Temporal Relation Extraction.

Cheng Cheng¹, Jeremy C Weiss²

¹Carnegie Mellon University, Pittsburgh, PA, United States.

Proceedings of Machine Learning Research

|March 13, 2024

Summary

This summary is machine-generated.

This study introduces typed markers for clinical temporal relation extraction, improving accuracy on the I2B2 dataset. The new method enhances transformer models by incorporating medical entity information, achieving state-of-the-art results.

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Area of Science:

Natural Language Processing
Clinical Informatics
Biomedical Text Mining

Background:

Accurate extraction of temporal relations from clinical notes is crucial for clinical research.
Existing methods often require complex feature engineering and temporal reasoning.

Purpose of the Study:

To introduce and evaluate a novel approach using typed markers for clinical temporal relation extraction.
To improve the performance of transformer-based models by integrating medical entity information.

Main Methods:

Developed strategies for creating typed markers incorporating entity type information at various granularities.
Applied these typed markers to clinical text, creating tagged context sentences.
Input tagged sentences into a transformer-based architecture for relation extraction.

Main Results:

The proposed system achieved the best reported F1 score of 83.5% on the I2B2 clinical benchmark dataset.
Demonstrated a substantial 3.3% improvement over the previous best system.
Showcased that typed markers outperform more complex systems without extensive feature engineering.

Conclusions:

Typed markers, when integrated with medical entity information, significantly enhance clinical temporal relation extraction.
This approach offers a more effective and potentially simpler alternative to existing complex systems.
The findings pave the way for more reliable temporal information extraction in clinical research.