FlauBERT vs. CamemBERT: Understanding patient's answers by a French medical chatbot

Blanc, Coralie; Bailly, Alexandre; Francis, Élie; Guillotin, Thierry; Jamal, F; Wakim, Béchara; Roy, Pascal

doi:10.1016/j.artmed.2022.102264

Cited by 16 publications

(8 citation statements)

References 4 publications

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“…The objective was to perform unsupervised learning, while we focused on supervised learning. Blanc et al [ 27 ] compared the performances of CamemBERT and FlauBERT to build a chatbot, and Sauvayre et al [ 28 ] used CamemBERT to classify the opinion of Twitter users on COVID-19 vaccines. The BERT language model is therefore usable for the French language and could be adapted for the classification of tweets.…”

Section: Introductionmentioning

confidence: 99%

Users’ Reactions to Announced Vaccines Against COVID-19 Before Marketing in France: Analysis of Twitter Posts

Dupuy-Zini¹,

Gérardin²,

Duclos³

et al. 2023

J Med Internet Res

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Background Within a few months, the COVID-19 pandemic had spread to many countries and had been a real challenge for health systems all around the world. This unprecedented crisis has led to a surge of online discussions about potential cures for the disease. Among them, vaccines have been at the heart of the debates and have faced lack of confidence before marketing in France. Objective This study aims to identify and investigate the opinions of French Twitter users on the announced vaccines against COVID-19 through sentiment analysis. Methods This study was conducted in 2 phases. First, we filtered a collection of tweets related to COVID-19 available on Twitter from February 2020 to August 2020 with a set of keywords associated with vaccine mistrust using word embeddings. Second, we performed sentiment analysis using deep learning to identify the characteristics of vaccine mistrust. The model was trained on a hand-labeled subset of 4548 tweets. Results A set of 69 relevant keywords were identified as the semantic concept of the word “vaccin” (vaccine in French) and focused mainly on conspiracies, pharmaceutical companies, and alternative treatments. Those keywords enabled us to extract nearly 350,000 tweets in French. The sentiment analysis model achieved 0.75 accuracy. The model then predicted 16% of positive tweets, 41% of negative tweets, and 43% of neutral tweets. This allowed us to explore the semantic concepts of positive and negative tweets and to plot the trends of each sentiment. The main negative rhetoric identified from users’ tweets was that vaccines are perceived as having a political purpose and that COVID-19 is a commercial argument for the pharmaceutical companies. Conclusions Twitter might be a useful tool to investigate the arguments for vaccine mistrust because it unveils political criticism contrasting with the usual concerns on adverse drug reactions. As the opposition rhetoric is more consistent and more widely spread than the positive rhetoric, we believe that this research provides effective tools to help health authorities better characterize the risk of vaccine mistrust.

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Section: Introductionmentioning

confidence: 99%

Users’ Reactions to Announced Vaccines Against COVID-19 Before Marketing in France: Analysis of Twitter Posts

Dupuy-Zini¹,

Gérardin²,

Duclos³

et al. 2023

J Med Internet Res

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“…Luo et al 48 developed the BioGPT language model, a domain-specific generative Transformer that was trained on a large volume of biological literature and tested on six biomedical natural language processing tasks. Blanc et al 49 used FlauBERT and CamemBERT to design a study to ascertain which language model and neural network architecture combination was best for intent and slot prediction by a chatbot using a French corpora of clinical cases.…”

Section: Related Workmentioning

confidence: 99%

Knowledge grounded medical dialogue generation using augmented graphs

Varshney

Zafar

Behera

et al. 2023

Sci Rep

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Smart healthcare systems that make use of abundant health data can improve access to healthcare services, reduce medical costs and provide consistently high-quality patient care. Medical dialogue systems that generate medically appropriate and human-like conversations have been developed using various pre-trained language models and a large-scale medical knowledge base based on Unified Medical Language System (UMLS). However, most of the knowledge-grounded dialogue models only use local structure in the observed triples, which suffer from knowledge graph incompleteness and hence cannot incorporate any information from dialogue history while creating entity embeddings. As a result, the performance of such models decreases significantly. To address this problem, we propose a general method to embed the triples in each graph into large-scalable models and thereby generate clinically correct responses based on the conversation history using the recently recently released MedDialog(EN) dataset. Given a set of triples, we first mask the head entities from the triples overlapping with the patient’s utterance and then compute the cross-entropy loss against the triples’ respective tail entities while predicting the masked entity. This process results in a representation of the medical concepts from a graph capable of learning contextual information from dialogues, which ultimately aids in leading to the gold response. We also fine-tune the proposed Masked Entity Dialogue (MED) model on smaller corpora which contain dialogues focusing only on the Covid-19 disease named as the Covid Dataset. In addition, since UMLS and other existing medical graphs lack data-specific medical information, we re-curate and perform plausible augmentation of knowledge graphs using our newly created Medical Entity Prediction (MEP) model. Empirical results on the MedDialog(EN) and Covid Dataset demonstrate that our proposed model outperforms the state-of-the-art methods in terms of both automatic and human evaluation metrics.

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“…Nevertheless, the trends observed within this set of keywords are also reflected in the analysis provided in the following sections. [23], construction of cohorts of similar patients [24], processing of electronic medical records [25], understanding of patient's answers in a French medical chatbot [26]; • German: evaluation of Transformers on clinical notes [27]; • Greek: improving the performance of localized healthcare virtual assistants [28]; • Hindi: classification of COVID-19 texts [29], chatbot for information sexual and reproductive health for young people [30]; • Italian: analysis of social media for quality of life in Parkinson's patients [31], sentiment analysis of opinion on COVID-19 vaccines [32,33], estimation of the incidence of infectious disease cases [34]; • Japanese: understanding psychiatric illness [35], detection of adverse events from narrative clinical documents [36]; • Korean: BERT model for processing med-ical documents [37], sentiment analysis of tweets about COVID-19 vaccines [38];…”

Section: Analysis Of Abstract From Publicationsmentioning

confidence: 99%

Year 2022 in Medical Natural Language Processing: Availability of Language Models as a Step in the Democratization of NLP in the Biomedical Area

Grouin,

Grabar

2023

Yearb Med Inform

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Objectives: To analyse the content of publications within the medical Natural Language Processing (NLP) domain in 2022. Methods: Automatic and manual preselection of publications to be reviewed, and selection of the best NLP papers of the year. Analysis of the important issues. Results: Three best papers have been selected. We also propose an analysis of the content of the NLP publications in 2022, stressing on some of the topics. Conclusion: The main trend in 2022 is certainly related to the availability of large language models, especially those based on Transformers, and to their use by non-NLP researchers. This leads to the democratization of the NLP methods. We also observe the renewal of interest to languages other than English, the continuation of research on information extraction and prediction, the massive use of data from social media, and the consideration of needs and interests of patients.

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FlauBERT vs. CamemBERT: Understanding patient's answers by a French medical chatbot

Cited by 16 publications

References 4 publications

Users’ Reactions to Announced Vaccines Against COVID-19 Before Marketing in France: Analysis of Twitter Posts

Users’ Reactions to Announced Vaccines Against COVID-19 Before Marketing in France: Analysis of Twitter Posts

Knowledge grounded medical dialogue generation using augmented graphs

Year 2022 in Medical Natural Language Processing: Availability of Language Models as a Step in the Democratization of NLP in the Biomedical Area

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