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. © 2013 Springer-Verlag.
CITATION STYLE
Egho, E., Jay, N., Raïssi, C., Nuemi, G., Quantin, C., & Napoli, A. (2013). An approach for mining care trajectories for chronic diseases. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 7885 LNAI, pp. 258–267). Springer Verlag. https://doi.org/10.1007/978-3-642-38326-7_37
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