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Section: Partnerships and Cooperations

National Initiatives

ANR Kolflow: man-machine collaboration in continuous knowledge-construction flows

Participants : Jean Lieber [contact person] , Amedeo Napoli, Emmanuel Nauer, Julien Stévenot, Yannick Toussaint.

Kolflow (http://kolflow.univ-nantes.fr/ ) is a 3-years basic research project taking place from February 2011 to July 2014, funded by French National Agency for Research (ANR), program ANR CONTINT. The aim of the project is investigation on man-machine collaboration in continuous knowledge-construction flows. Kolflow partners are Edelweiss (INRIA Sophia Antipolis), GDD (LINA Nantes), Silex (LIRIS Lyon), Orpailleur, and Score (LORIA).

ANR Trajcan: a study of patient care trajectories

Participants : Elias Egho, Nicolas Jay [contact person] , Amedeo Napoli, Chedy Raïssi.

Since 30 years, many patient classification systems (PCS) have been developed. These systems aim at classifying care episodes into groups according to different patient characteristics. In France, the so-called “Programme de Médicalisation des Systèmes d'Information” (PMSI) is a national wide PCS in use in every hospital. It systematically collects data about millions of hospitalizations. Though it is used for funding purposes, it includes useful knowledge for other public health domains such as epidemiology or health care planning.

The objective of the Trajcan project is to represent and analyze “patient care trajectories” (patient suffering from cancer limited to breast, colon, rectum, and lung cancers) and the associated healthcares. The data are related to patients receiving hospital cares in the “Bourgogne” region and using data from the PMSI. Such an analysis involves various data, e.g. type of cancer, number of visits, type of stays, hospitalization services and therapies used, and demographic factors, i.e. age, gender, place of residence.

One thesis is currently carried out on this subject whose objective is to design a knowledge discovery system working on multidimensional and sequential data for characterizing Patient Care Trajectories (PCT). This thesis combines knowledge discovery and knowledge representation methods for improving the definition of patient care trajectories as temporal objects (sequential data mining). The overall objective id to provide in decision support for improving healthcare in detecting for example typical or exceptional trajectories for planning with precision healthcare for a given population. In order to discover groups of patients showing similar health condition, treatments or journeys through the healthcare system, PCT are mined with multilevel and multidimensional sequential itemsets search, using external knowledge on hospitals, medical procedures and diagnoses. FCA capabilities for dealing with large amounts of data and for filtering (with a measure such as stability) are then used as a post-processing step for selecting the most interesting patterns [46] .