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A Dynamic Bayesian Network for Handling Uncertainty in a Decision Support System Adapted to the Monitoring of Patients Treated by Hemodialysis

Cédric Rose 1 Chérif Smaili 1 François Charpillet 1
1 MAIA - Autonomous intelligent machine
INRIA Lorraine, LORIA - Laboratoire Lorrain de Recherche en Informatique et ses Applications
Abstract : Telemedicine is a mean of facilitating the distribution of human resources and professional competences. It can speed up diagnosis and therapeutic care delivery and allow peripheral healthcare providers to receive continuous assistance from specialized centers. The need of specialized human resources becomes critical with the aging of the population. The treatment of renal failure is an example where telemedicine can help to increase care quality. Over the last decades Bayesian networks has become a popular representation for encoding uncertain expert knowledge. Dynamic Bayesian networks are an extension of Bayesian networks for modeling dynamic processes. We developed a dynamic Bayesian network adapted to the monitoring of the dry weight of patients suffering from chronic renal failure treated by hemodialysis. An experimentation conducted at dialysis units indicated that the system is reliable and gets the approbation of its users.
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https://hal.inria.fr/inria-00000477
Contributor : Cédric Rose <>
Submitted on : Friday, October 21, 2005 - 3:04:33 PM
Last modification on : Friday, February 26, 2021 - 3:28:04 PM
Long-term archiving on: : Thursday, April 1, 2010 - 10:52:45 PM

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Cédric Rose, Chérif Smaili, François Charpillet. A Dynamic Bayesian Network for Handling Uncertainty in a Decision Support System Adapted to the Monitoring of Patients Treated by Hemodialysis. 17th IEEE International Conference on Tools with Artificial Intelligence - ICTAI'05, Nov 2005, Hong Kong/China. ⟨inria-00000477⟩

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