| 2026 | ICAART | Extending Temporal Case-Based Reasoning for Action-Conditioned Time Series Prediction from Mixed Asynchronous Data: Application to Data-Driven Medical Simulation. | Hugo Boisaubert, Lucas Vincent, Corinne Lejus-Bourdeau, Christine Sinoquet |
| 2025 | ESANN | Investigating four deep learning approaches as candidates for unified models in time series forecasting and event prediction: application in anesthesia training. | Quentin Victor, Ianis Clavier, Hugo Boisaubert, Fabien Picarougne, Corinne Lejus-Bourdeau, Christine Sinoquet |
| 2025 | IDA | Two-in-One Models for Event Prediction and Time Series Forecasting. Comparison of Four Deep Learning Approaches to Simulate a Digital Patient Under Anesthesia. | Quentin Victor, Ianis Clavier, Hugo Boisaubert, Fabien Picarougne, Corinne Lejus-Bourdeau, Christine Sinoquet |
| 2024 | ESANN | LSTM encoder-decoder model for contextualized time series forecasting applied to the simulation of a digital patient's physiological variables. | Julien Paris, Christine Sinoquet, Fadoua Taia-Alaoui, Corinne Lejus-Bourdeau |
| 2024 | KES | Deep joint modelling of mixed asynchronous streams - Proof of concept for data-driven simulation of a digital patient under anaesthesia. | Julien Paris, Christine Sinoquet, Fadoua Taia-Alaoui, Corinne Lejus-Bourdeau |
| 2023 | DSAA | A Framework for Context-Sensitive Prediction in Time Series - Feasibility Study for Data-Driven Simulation in Medicine. | Fatoumata Dama, Christine Sinoquet, Corinne Lejus-Bourdeau |
| 2023 | ESANN | A hidden Markov model with Hawkes process-derived contextual variables to improve time series prediction. Case study in medical simulation. | Fatoumata Dama, Christine Sinoquet, Corinne Lejus-Bourdeau |