Due to necessary scheduled maintenance, the JMIR Publications website will be unavailable from Wednesday, July 01, 2020 at 8:00 PM to 10:00 PM EST. We apologize in advance for any inconvenience this may cause you.
Who will be affected?
Readers: No access to all 28 journals. We recommend accessing our articles via PubMed Central
Authors: No access to the submission form or your user account.
Reviewers: No access to your user account. Please download manuscripts you are reviewing for offline reading before Wednesday, July 01, 2020 at 7:00 PM.
Editors: No access to your user account to assign reviewers or make decisions.
Copyeditors: No access to user account. Please download manuscripts you are copyediting before Wednesday, July 01, 2020 at 7:00 PM.
Warning: This is an author submission that is not peer-reviewed or edited. Preprints - unless they show as "accepted" - should not be relied on to guide clinical practice or health-related behavior and should not be reported in news media as established information.
Development and Validation of Deep Learning Transformer Models for Building a Comprehensive and Real-time Trauma Monitoring
Gabrielle Chenais;
Cédric Gil-Jardiné;
Hélène Touchais;
Marta Avalos Fernandez;
Benjamin Contrand;
Eric Tellier;
Xavier Combes;
Loick Bourdois;
Philippe Revel;
Emmanuel Lagarde
ABSTRACT
Background:
In order to study the feasibility of setting up a national trauma observatory in France,
Objective:
we compared the performance of several automatic language processing methods on a multi-class classification task of unstructured clinical notes.
Methods:
A total of 69,110 free-text clinical notes related to visits to the emergency departments of the University Hospital of Bordeaux, France, between 2012 and 2019 were manually annotated. Among those clinical notes 22,481 were traumas. We trained 4 transformer models (deep learning models that encompass attention mechanism) and compared them with the TF-IDF (Term- Frequency - Inverse Document Frequency) associated with SVM (Support Vector Machine) method.
Results:
The transformer models consistently performed better than TF-IDF/SVM. Among the transformers, the GPTanam model pre-trained with a French corpus with an additional auto-supervised learning step on 306,368 unlabeled clinical notes showed the best performance with a micro F1-score of 0.969.
Conclusions:
The transformers proved efficient multi-class classification task on narrative and medical data. Further steps for improvement should focus on abbreviations expansion and multiple outputs multi-class classification.
Citation
Please cite as:
Chenais G, Gil-Jardiné C, Touchais H, Avalos Fernandez M, Contrand B, Tellier E, Combes X, Bourdois L, Revel P, Lagarde E
Deep Learning Transformer Models for Building a Comprehensive and Real-time Trauma Observatory: Development and Validation Study