Virtual center for renal support: technological approach to patient physiological image |
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Authors: | Prado Manuel Roa Laura Reina-Tosina Javier Palma Alfonso Milán José Antonio |
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Affiliation: | Grupo de Ingeniería Biomédica, Universidad de Sevilla, 41092 Sevilla, Spain. mpradov@supercable.es |
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Abstract: | The patient physiological image (PPI) is a novel concept which manages the knowledge of the virtual center for renal support (VCRS), currently being developed by the Biomedical Engineering Group of the University of Seville. PPI is a virtual "replica" of the patient, built by means of a mathematical model, which represents several physiological subsystems of a renal patient. From a technical point of view, PPI is a component-oriented software module based on cutting-edge modeling and simulation technology. This paper provides a methodological and technological approach to the PPI. Computational architecture of PPI-based VCRS is also described. This is a multi-tier and multi-protocol system. Data are managed by several ORDBMS instances. Communications design is based on the virtual private network (VPN) concept. Renal patients have a minimum reliable access to the VCRS through a public switch telephone network--X.25 gateway. Design complies with the universal access requirement, allowing an efficient and inexpensive connection even in rural environments and reducing computational requirements in the patient's remote access unit. VCRS provides support for renal patients' healthcare, increasing the quality and quantity of monitored biomedical signals, predicting events as hypotension or low dialysis dose, assisting further to avoid them by an online therapy modification and easing diagnostic tasks. An online therapy adjustment experiment simulation is presented. Finally, the presented system serves as a computational aid for research in renal physiology. This is achieved by an open and reusable modeling and simulation architecture which allows the interaction among models and data from different scales and computer platforms, and a faster transference of investigation models toward clinical applications. |
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