Projets en cours
Droit, IA et données de santé (DRIAS)
CNRS (mission MITI), 2020
Artificial intelligence technologies are spreading in the fields of health, and it raises new questions about the legal responsibilities of public persons. The objective of this transdisciplinary project is to investigate the interactions between artificial intelligence (automatic and supported decisions, machine learning and big data analytics), health and administrative law.
Research Topics
- Artificial intelligence
- Machine learning
- Administrative responsabilities, administrative law
- Health data
Project members
- IRISA/Inria (Teams LACODAM and DRUID)
- IDPSP
- Centre Borely
- CERSA
- CHU Rennes & Brest
- EHESP
Projets passés
Pharmaco-Epidémiologie des Produits de Santé (PEPS)
Financé par ANSM, 2015-2018
L’objectif de ce projet est le développement d’une plate-forme pour la pharmaco-épidémiologie. La pharmaco-épidémiologie concerne l’usage et l’effet des médicaments en condition réelle. Le projet PEPS a pour but de développer des outils pour l’analyse de données provenant de la base du SNIIRAM, afin de conduire des études pharmaco-épidémiologiques.
Axes de recherche
- Boite à outils pour la fouille interactive de motifs séquentiels à base de requêtes
- Langage de requête adapté dirigé par le domaine
- Nouveaux opérateurs de fouille de motifs séquentiels (chroniques discriminantes)
- Application d’outils de fouille de données à la pharmaco-épidemiologie
Membres
- CHU Rennes & Brest
- EHESP
- LTSI
- IRT B<>Com
- IRISA/Inria (équipes LACODAM, DRUID and DYLISS)
Publications
- Erwan Drezen, Thomas Guyet, André Happe. From medico-administrative databases analysis to care trajectories analytics: an example with the French SNDS. Fundamental and Clinical Pharmacology, Wiley, 2017, hal-01631802
- Yann Dauxais, Thomas Guyet, David Gross-Amblard, André Happe. Discriminant chronicles mining: Application to care pathways analytics. Artificial Intelligence in Medicine, Jun 2017, Vienna, Austria. 2017, 16th Conference on Artificial Intelligence in Medicine. hal-01568929
- Thomas Guyet, André Happe, Yann Dauxais. Declarative Sequential Pattern Mining of Care Pathways. Conference on Artificial Intelligence in Medicine in Europe, Jun 2017, Vienna, Austria. 24, pp.1161 – 266, 2017, 16th Conference on Artificial Intelligence in Medicine., hal-01569023
HYPTSER: Hybrid-models for Time-Series Prediction
Funded by PGMO, 2017-2018
The objective of this project is to develop hybrid machine-learning models for time series prediction. It is applied to the prediction of KPI of cloud infrastructures.
Axes de recherche
- Time-series prediction
- Machine learning
Members
- Orange Labs, Lannion
- Inria (Teams LACODAM and LinkMedia)
ITRAMI
2014-2017
Axes de recherche
- Préférences utilisateur
- Nouveaux opérateurs de fouille de motifs : skypatterns
- Visualisation de données
Members
- LIG
- ST Microelectronics
SePaDec: Sequential Pattern Mining with Declarative Approaches
Financé par le projet PEPS et la Région Bretagne, 2016-2017
Axes de recherche
- Fouille de données déclarative
- Answer Set Programming
- Pharmaco-épidémiologie
Membres
- LACODAM
Publications
- Ahmed Samet, Thomas Guyet, Benjamin Negrevergne. Mining rare sequential patterns with ASP. ILP 2017 – 27th International Conference on Inductive Logic Programming, 2017, hal-01569582
- Ahmed Samet, Thomas Guyet, Benjamin Negrevergne, Tien-Tuan Dao, Tuan Nha Hoang, et al.. Expert Opinion Extraction from a Biomedical Database. Conference on Symbolic and Quantitative Approaches to Reasoning with Uncertainty (ECSQARU), Jul 2017, Lugano, Switzerland. Springer, 31 (LNCS 10369), pp.1 – 12, 2017, Proceedings of 14th European Conference on Symbolic and Quantitative Approaches to Reasoning with Uncertainty hal-01584984
