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 […]
Analyzing recurrent events in multiple sclerosis: a review of statistical models with application to the MSOAC database
Publisher: Journal of NeurologyAuthors: David Herman, Julien Tanniou, Emmanuelle Leray, Chloe Pierret, Quentin Pilard View Publication Abstract Patients with multiple sclerosis (MS) are susceptible to experience recurrent events of disability progression and relapses. Many studies still focus on analyzing MS events with traditional methods such as Cox proportional hazards, Poisson, and logistic regression that either […]
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 […]
Identification of risk profiles for liver injury in adults with multiple sclerosis using artificial intelligence method
Event: The Liver Meeting – AASLD (American Association for the Study of Liver Diseases) 2024, San Diego, California, USA Authors: Dominique Larrey, Fang Liz Zhou, Claire Brulle-Wohlhueter, Myriam Benamor, Jeffrey Chavin, Neda Razaz, Raphael Bejuit, Julien Dauriat, Théophile Reppelin, Romane Péan, Sam Ekhtiari, Nicolas Wagner, Fred Lublin View Poster Background The evolution of multiple sclerosis (MS) […]
Identification of risk profiles for liver injury in adults with multiple sclerosis using artificial intelligence method
Authors: Dominique Larrey, Fang Liz Zhou, Claire Brulle-Wohlhueter, Myriam Benamor, Jeffrey Chavin, Neda Razaz, Raphael Bejuit, Julien Dauriat, Théophile Reppelin, Romane Péan, Sam Ekhtiari, Nicolas Wagner, Fred Lublin Event: Congrès AFEF (Association Française pour l’Étude du Foie) View Poster Introduction et Objectif L’évolution du traitement de la sclérose en plaques (SEP) au cours des deux dernières […]
Analyzing Recurrent Events in Multiple Sclerosis: A Review of Statistical Models with Application to MSOAC Trial
Event: ECTRIMS 2024, Copenhagen, Denmark Authors: D. Herman, J. Tanniou, N. Lambert, P. Loustalot, Q. Pilard View Poster Introduction In Multiple Sclerosis (MS), patients are likely to face repeated event such as confirmed disability progression (CDP). However, clinical trials often focus on analyzing time to first events, ignoring subsequent events, which are of clinical importance, despite the availability of statistical […]
