Number of documents


Thomas Guyet

Research interests

My research area is artificial intelligence (AI) with a multidisciplinary approach - algorithmic, design methodologies and cognitive science. I am particularly interested in discovering spatial and temporal patterns in semantically rich datasets. My areas of application are related to agronomy (mainly landscapes) and health (care pathways analysis).

My first research direction is the temporal and spatial pattern mining. Data from the observation of living systems (agricultural and medical systems) have a strong spatial or temporal dimension. But the spatial and temporal information is often underutilized in the data mining algorithms. The challenge lies in identifying new kind of temporal/spatial patterns that have valuable properties to make possible their extraction by complete and correct algorithms. A recent approach I'm developping is the use declarative programming, more especially Answer Set Programming (ASP) with clingo, to mix pattern mining and reasonning.
My second research direction aims at better including the user in the loop of knowledge discovery. Specifically, I am interested in implementing interactive systems to support users in their exploration process of large datasets. To acheive this goal I propose an enactive point of view of the data interpretation that brings creative solutions to take into account the cognitive ergonomy of the knowledge discovery tools.

Yann Dauxais   

Conference papers5 documents

  • 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. pp.1161 - 266, ⟨10.1007/978-3-319-59758-4_29⟩. ⟨hal-01569023⟩
  • Yann Dauxais, David Gross-Amblard, Thomas Guyet, André Happe. Extraction de chroniques discriminantes. Extraction et Gestion des Connaissances (EGC), Jan 2017, Grenoble, France. ⟨hal-01413473⟩
  • 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. ⟨10.1007/978-3-319-59758-4₂6⟩. ⟨hal-01568929⟩
  • Frédéric Balusson, Marie-Anne Botrel, Olivier Dameron, Yann Dauxais, Erwan Drezen, et al.. PEPS: a platform for supporting studies in pharmaco-epidemiology using medico-administrative databases. International Congress on e-Health Research, Oct 2016, Paris, France. ⟨hal-01380939⟩
  • Yann Dauxais, David Gross-Amblard, Thomas Guyet, André Happe. Chronicles mining in a database of drugs exposures. ECML Doctoral consortium, Sep 2015, Porto, Portugal. ⟨hal-01184100⟩

Book sections1 document

  • Yann Dauxais, David Gross-Amblard, Thomas Guyet, André Happe. Discriminant chronicle mining. B. Pinaud, F. Guillet, F. Gandon and C. Largeron. Advances in Knowledge Discovery and Management (vol 8), Springer, Cham, pp.89--118, 2019, Advances in Knowledge Discovery and Management, 978-3-030-18128-4. ⟨10.1007/978-3-030-18129-1_5⟩. ⟨hal-01940146⟩