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Adapting Bidirectional Encoder Representations from Transformers (BERT) to Assess Clinical Semantic Textual

Klaus Kades^1,2, Jan Sellner^1,3, Gregor Koehler¹

¹German Cancer Research Center (DKFZ), Heidelberg, Germany.

JMIR Medical Informatics

|February 3, 2021

Summary

This summary is machine-generated.

This study optimized Bidirectional Encoder Representations from Transformers (BERT) for clinical text similarity. Combining M-Heads and graph-based approaches improved performance, demonstrating potential for domain-specific knowledge extrapolation.

Keywords:

National NLP Clinical Challenges Natural Language Processing clinical text mining semantic textual similarity

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Area of Science:

Natural Language Processing
Clinical Informatics
Machine Learning

Background:

Clinical text data requires Natural Language Understanding for information extraction.
Bidirectional Encoder Representations from Transformers (BERT) achieved state-of-the-art results in 2018.
The National NLP Clinical Challenges (n2c2) focuses on domain-specific clinical NLP tasks.

Purpose of the Study:

To optimize Bidirectional Encoder Representations from Transformers (BERT) for assessing semantic textual similarity in clinical data.
To enhance BERT's performance on clinical text similarity tasks through novel approaches.

Main Methods:

Utilized BERT as a baseline and developed three distinct enhancement strategies.
Implemented handcrafted sentence similarity features and multiple regression estimators.
Incorporated a novel M-Heads ensembling method and a graph-based similarity approach for medications.
Evaluated performance using the Pearson correlation coefficient against ground truth data.

Main Results:

Achieved an improved Pearson correlation coefficient from 0.859 to 0.883 on the test dataset.
Demonstrated performance gains by combining the M-Heads method with the graph-based similarity approach.
Analyzed dataset differences between training and testing sets and their impact on results.

Conclusions:

The graph-based similarity approach shows potential for extrapolating domain-specific knowledge to new clinical sentences.
Highlighted the risk of deceptive results when training and test dataset distributions differ significantly.