AliBERT: A pretrained Language Model for French Biomedical Text
Event : BioNLP 2023 Workshop at ACL (Association for Computational Linguistics)Authors: A. Berhe, G. Draznieks, V. Martenot, V. Masdeu, L. Davy, J-D. Zucker View publication Background Over the past few years, domain specific pretrained language models have been investigated and have shown remarkable achievements in different downstream tasks, especially in biomedical domain. These achievements stem on the well known BERT […]
LiSA : an assisted literature search pipeline for detecting serious adverse drug events with deep learning
Publisher: BMC Medical Informatics and Decision MakingAuthors: V. Martenot, V. Masdeu, J. Cupe, F. Gehin, M. Blanchon, J. Dauriat, A. Horst, M. Renaudin, P. Girard, J-D. Zucker View publication Introduction Detecting safety signals attributed to a drug in scientific literature is a fundamental issue in pharmacovigilance. The constant increase in the volume of publications requires the automation of this tedious task, […]
A Natural Language Processing (NLP) Approach to Automate Patients’ Testimonials Analysis
Event: ISPOR Europe 2022, Vienna, Austria Authors: P. Hayat, C. Clémente, V. Martenot, M. Rollot View Poster Objectives: Patients’ testimonials (e.g. posts on forums or responses to questionnaires) provide valuable insights to define and characterize patient-reported outcomes (PRO), quality of life and patients’ perspective of disease symptoms. However, traditional NLP methods used for automated analysis of patients’ […]
Une nouvelle approche d’analyse automatique de réponses de questionnaires patients basée sur les modèles de langages
Event: 14ème colloque annuel des Données de Santé en Vie Réelle 2022, AFCROs, Paris, France Authors: L. Deplante, P. Hayat, M. Rollot View Poster Introduction Les questionnaires à réponse ouverte représentent un outil précieux de la recherche épidémiologique pour recueillir la perception des patients sur leur maladie, leur qualité de vie ou leur prise en charge, […]
Using Natural Language Processing (NLP) to understand self-reported barriers and enablers to treatment adherence in type 2 diabetes (T2D) using data from the International Diabetes Management Practices Study (IDMPS)
Publisher: ADA 2022, New Orleans, LA, USA Authors: J.C. Chan; J.J. Gagliardino; H.M. Ilkova; A. Ramachandran; J-C. Mbanya; M. V. Shestakova; M. Rollot; M. Blanchon; P. Hayat; J-M. Chantelot; P. Aschner View Poster Introduction IDMPS is an international, observational study to assess care practices and clinical profiles of people with diabetes in developing countries. […]
