Policy makers must adopt agile signal detection tools to strengthen epidemiological surveillance and improve pandemic preparedness
Publisher: Health Policy Authors: Cédric Mahé, Aimo Kannt, AIOLOS Consortium View publication Abstract The SARS-COV2 pandemic has highlighted the urgent need for agile and responsive disease surveillance systems. To strengthen epidemiological surveillance and improve pandemic preparedness, policymakers must adopt real-time signal detection tools that integrate multisource data, including non-traditional health data, advanced analytics, and artificial […]
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 […]
Comparison of Bayesian methods for extrapolation of treatment effects: a large scale simulation study
Publisher: arXivAuthors: Tristan Fauvel, Julien Tanniou, Pascal Godbillot, Billy Amzal View Publication Abstract Extrapolating treatment effects from related studies is a promising strategy for designing and analyzing clinical trials in situations where achieving an adequate sample size is challenging. Bayesian methods are well-suited for this purpose, as they enable the synthesis of prior information through the use of […]
AI-Driven Disease and Care Modeling and Simulation: A Framework for Simulating Real-World Scenarios in Non-Small Cell Lung Cancer (NSCLC)
Event: ELCC 2025, Paris, France Authors: Antoine Movschin, Lise Bosquet, David Pérol, Melissa Rollot, Louise Dry, Coriande Clemente, Farah Al Nakib, Mathilde Berthelot, Margaux Törnqvist View Poster Background Advances in personalized medicine have driven targeted therapy for NSCLC through mutation identification, but their rarity in real-world (RW) settings limits traditional RCTs. External control arms (ECAs) offer an […]
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 […]
Artificial Intelligence (AI) ToOLs for Outbreak Detection and response: a transnational platform for surveillance, monitoring and decision support
Event: ISPOR Europe 2023, Copenhagen, Denmark Authors: M. Génin, J. Botz, L. Coudeville, M. Eisenlauer, T. Fauvel, F. Huschka, N. Lambert, D. Wang, V. Bosch Castells, C. Commaille-Chapus, B. Frandji, M. Haberstroh, JY. Robin, P. Roehn, H. Sippel, P. Thiele, E. Thommes, C. Weber, B. Amzal, H. Fröhlich, A. Kannt, C. Mahé View Poster Objectives […]
