InforLorV4, Hal, Curation, bibRecord, 000D64

An approach for mining care trajectories for chronic diseases

Identifieur interne : 000D64 ( Hal/Curation ); précédent : 000D63; suivant : 000D65

An approach for mining care trajectories for chronic diseases

Auteurs : Elias Egho [France] ; Nicolas Jay [France] ; Chedy Raïssi [France] ; Gilles Nuemi [France] ; Catherine Quantin [France] ; Amedeo Napoli [France]

Source :

RBID : Hal:hal-00883117

English descriptors

mix :
- chronic illness, claim data, datamining, sequential pattern mining, trajectory of care.

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.

Url:

https://hal.inria.fr/hal-00883117

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Le document en format XML

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<front><div type="abstract" xml:lang="en">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.</div>
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<abstract xml:lang="en">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.</abstract>
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An approach for mining care trajectories for chronic diseases

An approach for mining care trajectories for chronic diseases

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