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Conference Papers Year : 2013

An approach for mining care trajectories for chronic diseases

Elias Egho
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Nicolas Jay
Chedy Raïssi
Amedeo Napoli


With the increasing burden of chronic illnesses, administrative health care databases hold valuable information that could be used to monitor and assess the processes shaping the trajectory of care of chronic patients. In this context, temporal data mining methods are promising tools, though lacking flexibility in addressing the complex nature of medical events. Here, we present a new algorithm able to extract patient trajectory patterns with different levels of granularity by relying on external taxonomies. We show the interest of our approach with the analysis of trajectories of care for colorectal cancer using data from the French casemix information system.
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hal-00883117 , version 1 (09-11-2013)


  • HAL Id : hal-00883117 , version 1


Elias Egho, Nicolas Jay, Chedy Raïssi, Gilles Nuemi, Catherine Quantin, et al.. An approach for mining care trajectories for chronic diseases. AIME 2013 - 14th Conference on Artificial Intelligence in Medicine, May 2013, Murcia, Spain. ⟨hal-00883117⟩
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