Efficient extraction of medication information from clinical notes: an evaluation in two languages
Journal:
arXiv
Published Date:
Feb 5, 2025
Abstract
Objective: To evaluate the accuracy, computational cost and portability of a
new Natural Language Processing (NLP) method for extracting medication
information from clinical narratives. Materials and Methods: We propose an
original transformer-based architecture for the extraction of entities and
their relations pertaining to patients' medication regimen. First, we used this
approach to train and evaluate a model on French clinical notes, using a newly
annotated corpus from H\^opitaux Universitaires de Strasbourg. Second, the
portability of the approach was assessed by conducting an evaluation on
clinical documents in English from the 2018 n2c2 shared task. Information
extraction accuracy and computational cost were assessed by comparison with an
available method using transformers. Results: The proposed architecture
achieves on the task of relation extraction itself performance that are
competitive with the state-of-the-art on both French and English (F-measures
0.82 and 0.96 vs 0.81 and 0.95), but reduce the computational cost by 10.
End-to-end (Named Entity recognition and Relation Extraction) F1 performance is
0.69 and 0.82 for French and English corpus. Discussion: While an existing
system developed for English notes was deployed in a French hospital setting
with reasonable effort, we found that an alternative architecture offered
end-to-end drug information extraction with comparable extraction performance
and lower computational impact for both French and English clinical text
processing, respectively. Conclusion: The proposed architecture can be used to
extract medication information from clinical text with high performance and low
computational cost and consequently suits with usually limited hospital IT
resources
