Performance en classification de données textuelles des passages aux urgences des modèles BERT pour le français

National audienceContextualized language models based on the Transformer architecture such as BERT (Bidirectional Encoder Representations from Transformers) have achieved remarkable performances in various language processing tasks. CamemBERT and FlauBERT are pre-trained versions for French.We used these two models to automatically classify free clinical notes from emergency department visits following a trauma. Their performances were compared to the TF-IDF (Term-Frequency - Inverse Document Frequency) method associated with the SVM (Support Vector Machine) classifier on 22481 clinical notes from the emergency department of the Bordeaux University Hospital. CamemBERT and FlauBERT obtained slightly better results than the TF-IDF/SVM couple for the micro F1-score. These encouraging results allow us to consider further developments in the use of transformers in the automation of emergency department data processing in order to consider the implementation of a national observatory of trauma in France.Les modèles de langue contextualisés basés sur l'architecture Transformer tels que BERT (Bidirectional Encoder Representations from Transformers) ont atteint des performances remarquables dans des diverses tâches de traitement de la langue. CamemBERT et FlauBERT en sont des versions pré-entraînées pour le français. Nous avons utilisé ces deux modèles afin de classer automatiquement des notes cliniques libres issues de visites aux urgences à la suite d'un traumatisme. Leurs performances ont été comparées à la méthode TF-IDF (Term-Frequency-Inverse Document Frequency) associé au classifieur SVM (Support Vector Machine) sur 22481 notes cliniques provenant du service des urgences du CHU de Bordeaux. CamemBERT et FlauBERT ont obtenu des résultats légèrement supérieurs à ceux du couple TF-IDF/SVM pour le micro F1-score. Ces résultats encourageants permettent d'envisager l'utilisation des transformers pour automatiser le traitement des données des urgences dans le cadre de la mise en place d'un observatoire national du traumatisme en France

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

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

Risk factors, risk assessment and prevention of post-traumatic stress syndrome after emergency care

Dans le monde entier, des dizaines de millions de personnes sont victimes de blessures mineures et beaucoup d'entre elles sont admises aux urgences. Cela représente chaque année environ 5 millions d'admissions aux urgences en France et près de 40 millions en Europe. Depuis plusieurs années, des études suggèrent que jusqu'à 20 % de ces patients souffriront pendant des mois de symptômes chroniques décrits initialement dans le traumatisme crânien léger (TCL) et appelés ainsi « Syndrome post-commotionnel » (SPC). Aujourd’hui, ces symptômes ont été identifié comme non spécifique du TCL et la plupart des auteurs utilise le terme de « Post-Concussion-Like Symptoms » (PCLS). Une telle combinaison de symptômes peut entraîner une détérioration importante de la qualité de vie sociale et familiale ou retarder le retour au travail ou à l'école. Rien qu'en France, si les résultats décrits dans la littérature sont représentatifs de l'ensemble de la population, jusqu'à un million de personnes pourrait être concernées par cette problématique actuellement mal identifiée de santé publique.Les différents objectifs de ce travail de thèse étaient ainsi :Identifier les facteurs associés à l’apparition de « Post-Concussion like symptoms » à distance d’un passage aux urgences,Élaborer un outil d’évaluation du niveau de risque de développer ces symptômes pour les patients pris en charge aux urgencesIdentifier les interventions qui pourraient être proposer aux urgences comme moyen de prévention.Évaluer l’intérêt de la mise en place d’interventions au cours du passage aux urgences pour prévenir la survenue de ces symptômes.Nous avons retrouvé dans SOFTER 1 que les PCLS à 4 mois sont associés au stress à la sortie des urgences. Puis grâce à l’élaboration d’un outil d’évaluation du niveau de risque, nous avons montré qu’il est possible de conduire des séances d’EMDR au cours du séjour dans les urgences. L’efficacité de cette intervention semblerait en revanche influencée par de nombreux facteurs comme le niveau socio-économique des patients, leur niveau de stress et l’expérience des psychologues.Ainsi, les résultats actuellement disponibles suggèrent que les structures d’urgences pourraient être un lieu privilégié pour repérer et prendre en charge des patients fragiles, à risque de développe des PCLS. L’opportunité offerte par le passage aux urgences pourrait avoir un impact important en termes de santé publique et constituer un outil puissant de santé communautaire pour lutter contre les inégalités de santé.Worldwide, tens of millions of people suffer minor injuries and many are admitted to emergency departments (ED). This represents approximately 5 million ED admissions in France and nearly 40 million in Europe each year. For several years, studies have suggested that up to 20% of these patients will suffer for months from chronic symptoms initially described in mild traumatic brain injury (MTBI) and referred to as "post-concussion syndrome" (PCS). Today, these symptoms have been identified as non-specific to TCL and most authors use the term "Post-Concussion-Like Symptoms" (PCLS). Such a combination of symptoms can lead to a significant deterioration in the quality of social and family life or delay the return to work or school. In France, if the results described in the literature are representative of the entire population, up to one million people could be affected by this currently poorly identified public health problem.The different objectives of this work were as follows:- to identify the factors associated with the development of "Post-Concussion like symptoms" at a distance from an emergency room visit,- to develop a tool to assess the level of risk of developing these symptoms for patients managed in emergency departments- to identify interventions that could be offered to emergencies as a means of prevention.- to assess the value of implementing interventions in the ED to prevent these symptoms from occurring.We found in SOFTER 1 that PCLS were associated with stress at the ED discharge. Then, after creating a risk assessment tool, we showed that it is possible to conduct EMDR sessions during ED stay. The effectiveness of this intervention appeared to be influenced by many factors such as patients' socio-economic conditions, stress level and psychologists' experience.Thus, results currently available suggested that ED could be a place to identify and manage fragile patients at risk of developing PCLS. The opportunity offered by ED visit could have a significant impact in terms of public health and could be a powerful community health tool to combat health inequalities

Facteurs de risque, dépistage et prévention des syndromes post-traumatiques à la suite d'un passage aux urgences

Worldwide, tens of millions of people suffer minor injuries and many are admitted to emergency departments (ED). This represents approximately 5 million ED admissions in France and nearly 40 million in Europe each year. For several years, studies have suggested that up to 20% of these patients will suffer for months from chronic symptoms initially described in mild traumatic brain injury (MTBI) and referred to as "post-concussion syndrome" (PCS). Today, these symptoms have been identified as non-specific to TCL and most authors use the term "Post-Concussion-Like Symptoms" (PCLS). Such a combination of symptoms can lead to a significant deterioration in the quality of social and family life or delay the return to work or school. In France, if the results described in the literature are representative of the entire population, up to one million people could be affected by this currently poorly identified public health problem.The different objectives of this work were as follows:- to identify the factors associated with the development of "Post-Concussion like symptoms" at a distance from an emergency room visit,- to develop a tool to assess the level of risk of developing these symptoms for patients managed in emergency departments- to identify interventions that could be offered to emergencies as a means of prevention.- to assess the value of implementing interventions in the ED to prevent these symptoms from occurring.We found in SOFTER 1 that PCLS were associated with stress at the ED discharge. Then, after creating a risk assessment tool, we showed that it is possible to conduct EMDR sessions during ED stay. The effectiveness of this intervention appeared to be influenced by many factors such as patients' socio-economic conditions, stress level and psychologists' experience.Thus, results currently available suggested that ED could be a place to identify and manage fragile patients at risk of developing PCLS. The opportunity offered by ED visit could have a significant impact in terms of public health and could be a powerful community health tool to combat health inequalities.Dans le monde entier, des dizaines de millions de personnes sont victimes de blessures mineures et beaucoup d'entre elles sont admises aux urgences. Cela représente chaque année environ 5 millions d'admissions aux urgences en France et près de 40 millions en Europe. Depuis plusieurs années, des études suggèrent que jusqu'à 20 % de ces patients souffriront pendant des mois de symptômes chroniques décrits initialement dans le traumatisme crânien léger (TCL) et appelés ainsi « Syndrome post-commotionnel » (SPC). Aujourd’hui, ces symptômes ont été identifié comme non spécifique du TCL et la plupart des auteurs utilise le terme de « Post-Concussion-Like Symptoms » (PCLS). Une telle combinaison de symptômes peut entraîner une détérioration importante de la qualité de vie sociale et familiale ou retarder le retour au travail ou à l'école. Rien qu'en France, si les résultats décrits dans la littérature sont représentatifs de l'ensemble de la population, jusqu'à un million de personnes pourrait être concernées par cette problématique actuellement mal identifiée de santé publique.Les différents objectifs de ce travail de thèse étaient ainsi :Identifier les facteurs associés à l’apparition de « Post-Concussion like symptoms » à distance d’un passage aux urgences,Élaborer un outil d’évaluation du niveau de risque de développer ces symptômes pour les patients pris en charge aux urgencesIdentifier les interventions qui pourraient être proposer aux urgences comme moyen de prévention.Évaluer l’intérêt de la mise en place d’interventions au cours du passage aux urgences pour prévenir la survenue de ces symptômes.Nous avons retrouvé dans SOFTER 1 que les PCLS à 4 mois sont associés au stress à la sortie des urgences. Puis grâce à l’élaboration d’un outil d’évaluation du niveau de risque, nous avons montré qu’il est possible de conduire des séances d’EMDR au cours du séjour dans les urgences. L’efficacité de cette intervention semblerait en revanche influencée par de nombreux facteurs comme le niveau socio-économique des patients, leur niveau de stress et l’expérience des psychologues.Ainsi, les résultats actuellement disponibles suggèrent que les structures d’urgences pourraient être un lieu privilégié pour repérer et prendre en charge des patients fragiles, à risque de développe des PCLS. L’opportunité offerte par le passage aux urgences pourrait avoir un impact important en termes de santé publique et constituer un outil puissant de santé communautaire pour lutter contre les inégalités de santé

The distracted mind on the wheel: Overall propensity to mind wandering is associated with road crash responsibility.

The role of distractions on attentional lapses that place road users in higher risk of crash remains poorly understood. We aimed to assess the respective impact of (i) mind wandering trait (propensity to mind wander in the everyday life as measured with a set of 4 questions on the proportion of time spent mind wandering in 4 different situations) and (ii) mind wandering state (disturbing thoughts just before the crash) on road crash risk using a comparison between responsible and non-responsible drivers. 954 drivers injured in a road crash were interviewed at the adult emergency department of the Bordeaux university hospital in France (2013-2015). Responsibility for the crash, mind wandering (trait/state), external distraction, alcohol use, psychotropic drug use, and sleep deprivation were evaluated. Based on questionnaire reports, 39% of respondents were classified with a mind wandering trait and 13% reported a disturbing thought just before the crash. While strongly correlated, mind wandering state and trait were independently associated with responsibility for a traffic crash (State: OR = 2.51, 95% CI: 1.64-3.83 and Trait: OR = 1.62, 95% CI: 1.22-2.16 respectively). Self-report of distracting thoughts therefore did not capture the entire risk associated with the propensity of the mind to wander, either because of under-reported thoughts and/or other deleterious mechanisms to be further explored

Serious Games for Training in Patient Flow Management in Emergency Departments: State of the Art and Perspectives

International audienceEmergency departments (EDs) face significant challenges in providing timely care due to the increase in patient volume and limited resources. To improve patient flow management, new strategies based on artificial intelligence, machine learning, computer modeling, and simulation have been developed, including serious computer games and virtual reality. We performed a systematic review of the use of serious games and virtual reality to train healthcare professionals in the ED

Neural Language Model for Automated Classification of Electronic Medical Records at the Emergency Room. The Significant Benefit of Unsupervised Generative Pre-training

In order to build a national injury surveillance system based on emergency room (ER) visits we are developing a coding system to classify their causes from clinical notes in free-text. Supervised learning techniques have shown good results in this area but require large number of annotated dataset. New levels of performance have been recently achieved in neural language models (NLM) with models based on the Transformer architecture incorporating an unsupervised generative pre-training step. Our hypothesis is that methods involving a generative self-supervised pre-training step can significantly reduce the required number of annotated samples for supervised fine-tuning. In this case study, we assessed whether we could predict from free-text clinical notes whether a visit was the consequence of a traumatic or non-traumatic event. Using fully re-trained GPT-2 models (without OpenAI pre-trained weightings), we compared two scenarios: Scenario A (26 study cases of different training data sizes) consisted in training the GPT-2 on the trauma/non-trauma labeled (up to 161 930) clinical notes. In Scenario B (19 study cases), a first step of self-supervised pre-training phase with unlabeled (up to 151 930) notes and the second step of supervised fine-tuning with labeled (up to 10 000) notes. Results showed that, Scenario A needed to process >6 000 notes to achieve good performance (AUC>0.95), Scenario B needed only 600 notes, gain of a factor 10. At the end case of both scenarios, for 16 times more data (161 930 vs. 10 000), the gain from Scenario A compared to Scenario B is only an improvement of 0.89% in AUC and 2.12% in F1 score. To conclude, it is possible to adapt a multi-purpose NLM model such as the GPT-2 to create a powerful tool for classification of free-text notes with only very small number of labeled samples

Pre-Training a Neural Language Model Improves the Sample Efficiency of an Emergency Room Classification Model

International audienceTo build a French national electronic injury surveillance system based on emergency room visits, we aim to develop a coding system to classify their causes from clinical notes in free-text. Supervised learning techniques have shown good results in this area but require a large amount of expert annotated dataset which is time consuming and costly to obtain. We hypothesize that the Natural Language Processing Transformer model incorporating a generative self-supervised pre-training step can significantly reduce the required number of annotated samples for supervised fine-tuning. In this preliminary study, we test our hypothesis in the simplified problem of predicting whether a visit is the consequence of a traumatic event or not from free-text clinical notes. Using fully retrained GPT-2 models (without OpenAI pre-trained weights), we assess the gain of applying a self-supervised pre-training phase with unlabeled notes prior to the supervised learning task. Results show that the number of data required to achieve a ginve level of performance (AUC>0.95) was reduced by a factor of 10 when applying pre-training. Namely, for 16 times more data, the fully-supervised model achieved an improvement <1% in AUC. To conclude, it is possible to adapt a multipurpose neural language model such as the GPT-2 to create a powerful tool for classification of free-text notes with only a small number of labeled samples

Characteristics of drowning victims in a surf environment: a 6-year retrospective study in southwestern France

Abstract Background Drowning is the third cause of non-intentional injury death worldwide. Beaches of Gironde, in southwestern France, are exposed to strong environmental conditions, leading to rip currents and shore breaks. Bathing season usually lasts from April to October and is supervised from June till mid-September. The objective of this study was to study the characteristics of drowning victims along Gironde surf beaches and to identify peculiarities compared to national figures. Methods All calls originating from beaches to the emergency call center of Gironde from 2011 to 2016 were analyzed. Patient data, filled by a physician based on information given by pre-hospital care team (lifeguards, paramedics or emergency physicians), were extracted from the emergency call center database. We used Szpilman classification (0 = rescue to 6 = cardiac arrest) to assess severity. Rescues are patients without respiratory impairment who needed lifeguards or helicopter intervention. We compared our findings with national studies carried every three years (2012 and 2015). Results We analyzed 5680 calls from beaches and included 4398, 576 of which were rescued from the water, including 352 without respiratory impairment (stage 0). Among drownings, 155 had cough only (stage 1), 26 pulmonary rales (stage 2), 9 pulmonary edema (stage 3) and 1 had pulmonary edema with hypotension (stage 4). Five rescued people were in respiratory arrest and 28 were in cardiac arrest. 77.5% were bathers, others were mainly surfers or body-boarders. Drowning victims median age was 24 (quartiles: 17–40), and sex-ratio was 1.44 Male/Female. Men were significantly older than women (34 vs. 26 years old), and severity from stage 1 to 4 was positively associated with age. Compared to national data, Gironde drownings had a higher proportion of 15–44 year-old victims, and the case-fatality was lower in Gironde (11.5%) than at the national level (27.4%, p < 0.001). Conclusion Along Gironde coast, drowning is rarely severe, concerns mostly young men; the age distribution could explain the different case-fatality. Further study is needed to identify environmental predictors of drowning

De-identification of Emergency Medical Records in French: Survey and Comparison of State-of-the-Art Automated Systems

International audienceIn France, structured data from emergency room (ER) visits are aggregated at the national level to build a syndromic surveillance system for several health events. For visits motivated by a traumatic event, information on the causes are stored in free-text clinical notes. To exploit these data, an automated de-identification system guaranteeing protection of privacy is required.In this study we review available de-identification tools to de-identify free-text clinical documents in French. A key point is how to overcome the resource barrier that hampers NLP applications in languages other than English. We compare rule-based, named entity recognition, new Transformer-based deep learning and hybrid systems using, when required, a fine-tuning set of 30,000 unlabeled clinical notes. The evaluation is performed on a test set of 3,000 manually annotated notes.Hybrid systems, combining capabilities in complementary tasks, show the best performance. This work is a first step in the foundation of a national surveillance system based on the exhaustive collection of ER visits reports for automated trauma monitoring