Remote monitoring of chronical pathologies using personalized Markov models
| Autor: | Laurent Jeanpierre, François Charpillet |
|---|---|
| Přispěvatelé: | Autonomous intelligent machine (MAIA), INRIA Lorraine, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Laboratoire Lorrain de Recherche en Informatique et ses Applications (LORIA), Institut National de Recherche en Informatique et en Automatique (Inria)-Université Henri Poincaré - Nancy 1 (UHP)-Université Nancy 2-Institut National Polytechnique de Lorraine (INPL)-Centre National de la Recherche Scientifique (CNRS)-Université Henri Poincaré - Nancy 1 (UHP)-Université Nancy 2-Institut National Polytechnique de Lorraine (INPL)-Centre National de la Recherche Scientifique (CNRS), Barrie W Jervis, Centre National de la Recherche Scientifique (CNRS)-Institut National Polytechnique de Lorraine (INPL)-Université Nancy 2-Université Henri Poincaré - Nancy 1 (UHP)-Institut National de Recherche en Informatique et en Automatique (Inria)-Centre National de la Recherche Scientifique (CNRS)-Institut National Polytechnique de Lorraine (INPL)-Université Nancy 2-Université Henri Poincaré - Nancy 1 (UHP) |
| Jazyk: | angličtina |
| Rok vydání: | 2003 |
| Předmět: | |
| Zdroj: | First International Conference on Computational Intelligence in Medicine and Healthcare 2003-CIMED 2003 First International Conference on Computational Intelligence in Medicine and Healthcare 2003-CIMED 2003, Barrie W Jervis, 2003, Sheffield, Angleterre, 6 p HAL |
| Popis: | Colloque avec actes et comité de lecture. internationale.; International audience; In this paper, an efficient method for diagnosing medical pathologies is presented, which is particularly adapted to long term monitoring of patients. It results from the collaboration between the LORIA (Lorrain Research Laboratory of Computer Science and its Applications), and the ALTIR (Lorrain Association for Renal Failure Treatments). An important aspect of our approach is that the physician can customize each patient's model. This model is expressed in medical terms, simplifying the interaction of physicians with the intelligent system. The approach will be illustrated with the remote monitoring of patients suffering from kidney disease. In a two years prospective randomized study, 15 patients have been monitored by our system, while 15 others were monitored the classical way. The two groups' statistics has shown that the system was really beneficent to patients' health. This experiment has led to the creation of the DIATELIC enterprise, to promote and develop this system. |
| Databáze: | OpenAIRE |
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