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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Egho, E., Jay, N., Raïssi, C., Nuemi, G., Quantin, C., Napoli, A. (2013). An Approach for Mining Care Trajectories for Chronic Diseases. In: Peek, N., Marín Morales, R., Peleg, M. (eds) Artificial Intelligence in Medicine. AIME 2013. Lecture Notes in Computer Science(), vol 7885. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-38326-7_37
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DOI: https://doi.org/10.1007/978-3-642-38326-7_37
Publisher Name: Springer, Berlin, Heidelberg
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