Patient-reported outcomes and treatment adherence in type 2 diabetes using natural language processing: Wave 8 of the Observational International Diabetes Management Practices Study
Publisher: Journal of Diabetes Investigation Authors: Juliana CN Chan, Jean Claude Mbanya, Jean-Marc Chantelot, Marina Shestakova, Ambady Ramachandran, Hasan Ilkova, Lucille Deplante, Melissa Rollot, Lydie Melas-Melt, Juan Jose Gagliardino, Pablo Aschner View Publication Aims/Introduction We analyzed patient-reported outcomes of people with type 2 diabetes to better understand perceptions and experiences contributing to treatment adherence. Materials and […]
An AI powered tool to identify and assess fit-for use registries for real-world evidence
Event: EuroDURG 2025, Uppsala, Sweden Authors: Ghinwa Y. Hayek, Pascal Godbillot, Coriande Clemente, Sonia Zebachi, Gaëtan Pinon, Boris Kopin, Elisabeth Bakker, Sieta T. de Vries, Peter G.M. Mol, Billy Amzal View publication Abstract The selection of appropriate real-world data (RWD) sources, particularly registries, is of primary concern for academia, industry, regulators, and health technology assessment […]
A text-to-tabular approach to generate synthetic patient data using LLMs
Publisher: IEEE XploreAuthors: Margaux Tornqvist, Jean-Daniel Zucker, Tristan Fauvel, Nicolas Lambert, Mathilde Berthelot, Antoine Movschin View publication Abstract Access to large-scale high-quality healthcare databases is key to accelerate medical research and make insightful discoveries about diseases. However, access to such data is often limited by patient privacy concerns, data sharing restrictions and high costs. To […]
A text-to-tabular approach to generate synthetic patient data using LLMs
Event: IEEE ICHI 2025, Rende, ItalyAuthors:Margaux Tornqvist, Jean-Daniel Zucker, Tristan Fauvel, Nicolas Lambert, Mathilde Berthelot, Antoine Movschin View Conference Proceeding Abstract Access to large-scale high-quality healthcare databases is key to accelerate medical research and make insightful discoveries about diseases. However, access to such data is often limited by patient privacy concerns, data sharing restrictions and […]
A text-to-tabular approach to generate synthetic patient data using LLMs
Publisher: arXivAuthors: Margaux Tornqvist, Jean-Daniel Zucker, Tristan Fauvel, Nicolas Lambert, Mathilde Berthelot, Antoine Movschin View publication Abstract Access to large-scale high-quality healthcare databases is key to accelerate medical research and make insightful discoveries about diseases. However, access to such data is often limited by patient privacy concerns, data sharing restrictions and high costs. To overcome […]
Existing practices for the identification, selection or assessment of registries for regulatory/HTA purposes: a More-EUROPA project systematic review
Event: ISPOR Europe 2023, Copenhagen, Denmark Authors: S. Zebachi, J. Moreira, J. Tanniou, P. Godbillot, P. Tang, B. Amzal View Poster Objectives Real-world data (RWD), particularly patient registries, represent a potential useful source of information and may be required at various stages of the drug lifecycle. Different questions may be asked throughout the regulatory and/or […]
Existing automated tools to assist evidence generation and better qualification of registries and real-world data. A systematic review from the More-EUROPA project
Event: ISPOR Europe 2023, Copenhagen, Denmark Authors: P. Godbillot, P. Tang, J. Moreira, J. Tanniou S. Zebachi, B. Amzal View Poster Objectives The More-EUROPA project consortium brings together 14 public and private entities from 7 countries with the aim of enhancing the ethical and effective utilization of registry data to facilitate patient-centered decision-making by drug regulators and Health Technology Assessment […]
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’ […]
