An approach for mining care trajectories for chronic diseases

Elias Egho 1 Nicolas Jay 1 Chedy Raïssi 1 Gilles Nuemi 2 Catherine Quantin 2 Amedeo Napoli 1
1 ORPAILLEUR - Knowledge representation, reasonning
Inria Nancy - Grand Est, LORIA - NLPKD - Department of Natural Language Processing & Knowledge Discovery
Abstract : 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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Communication dans un congrès
14th Conference on Artificial Intelligence in Medicine, May 2013, Murcia, Spain. Springer, 7885, 2013
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Dernière modification le : jeudi 11 janvier 2018 - 06:25:24
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Elias Egho, Nicolas Jay, Chedy Raïssi, Gilles Nuemi, Catherine Quantin, et al.. An approach for mining care trajectories for chronic diseases. 14th Conference on Artificial Intelligence in Medicine, May 2013, Murcia, Spain. Springer, 7885, 2013. 〈hal-00883117〉

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