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Deep Learning Transformer Models for Building a Comprehensive and Real-time Trauma Observatory: Development and

Gabrielle Chenais¹, Cédric Gil-Jardiné^1,2, Hélène Touchais¹

¹Unit 1219, Bordeaux Public Health Center, Institut National de la Santé et de la Recherche Médicale, Bordeaux, France.

|June 14, 2024

Summary

This summary is machine-generated.

Automated natural language processing (NLP) models, particularly transformers, show high efficiency in classifying unstructured clinical notes for public health surveillance. The GPTanam transformer model achieved the best performance in identifying trauma cases from electronic health records.

Keywords:

deep learning emergencies natural language processing public health transformers trauma

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Area of Science:

Medical Informatics
Natural Language Processing
Public Health Surveillance

Background:

Public health surveillance requires timely data collection.
Natural language processing (NLP) advances enable automated information extraction from electronic health records (EHRs).

Purpose of the Study:

To assess the feasibility of a national trauma observatory in France.
To compare NLP methods for multiclass classification of unstructured clinical notes.

Main Methods:

Utilized 69,110 clinical notes from French emergency departments (2012-2019).
Manually annotated notes for trauma classification (32.5% prevalence).
Trained 4 transformer models and compared them against TF-IDF with SVM.

Main Results:

Transformer models outperformed TF-IDF with SVM.
The GPTanam transformer model, pre-trained on a French corpus and fine-tuned with self-supervised learning, achieved the highest performance (micro F1-score of 0.969).

Conclusions:

Transformer models are effective for multiclass classification of clinical narrative data.
Future work should address abbreviation expansion and multi-output classification.