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End-to-end clinical temporal information extraction with multi-head attention.

Timothy Miller¹, Steven Bethard², Dmitriy Dligach³

¹Computational Health Informatics Program, Boston Children's Hospital, Harvard Medical School.

Proceedings of the Conference. Association for Computational Linguistics. Meeting

|October 2, 2023

Summary

This summary is machine-generated.

This study introduces a novel multi-headed attention mechanism for temporal relation extraction in clinical text. The system achieves state-of-the-art results on the THYME corpus, improving clinical applications.

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Area of Science:

Clinical Informatics
Natural Language Processing
Artificial Intelligence

Background:

Temporal relationship extraction from electronic health records (EHRs) is crucial for clinical applications.
Limited progress has been made in end-to-end temporal relation extraction systems since Clinical TempEval 2017.
Existing methods often rely on gold-standard annotations for events and time expressions.

Purpose of the Study:

To develop an advanced end-to-end system for temporal relation extraction from clinical text.
To improve the accuracy and applicability of temporal information in EHRs.
To address the limitations of previous approaches by not requiring gold-standard annotations.

Main Methods:

Utilized a novel multi-headed attention mechanism.
Integrated this mechanism with a pre-trained transformer encoder.
Enabled the model to attend to multiple facets of contextualized embeddings for enhanced learning.

Main Results:

Achieved state-of-the-art performance on the THYME corpus.
Demonstrated significant improvements in both in-domain and cross-domain settings.
Outperformed previous methods by a considerable margin.

Conclusions:

The proposed multi-headed attention mechanism significantly enhances temporal relation extraction.
The developed system offers a robust solution for analyzing temporal dynamics in clinical text.
This advancement holds promise for numerous downstream clinical applications.