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Mathematical Modeling And Validation In Physiology Applications To The Cardiovascular And Respiratory Systems 1st Edition Jerry J Batzel

  • SKU: BELL-4290148
Mathematical Modeling And Validation In Physiology Applications To The Cardiovascular And Respiratory Systems 1st Edition Jerry J Batzel
$ 31.00 $ 45.00 (-31%)

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Mathematical Modeling And Validation In Physiology Applications To The Cardiovascular And Respiratory Systems 1st Edition Jerry J Batzel instant download after payment.

Publisher: Springer-Verlag Berlin Heidelberg
File Extension: PDF
File size: 4.2 MB
Pages: 254
Author: Jerry J. Batzel, Mostafa Bachar, John M. Karemaker, Franz Kappel (auth.), Jerry J. Batzel, Mostafa Bachar, Franz Kappel (eds.)
ISBN: 9783642328817, 9783642328824, 3642328814, 3642328822
Language: English
Year: 2013
Edition: 1

Product desciption

Mathematical Modeling And Validation In Physiology Applications To The Cardiovascular And Respiratory Systems 1st Edition Jerry J Batzel by Jerry J. Batzel, Mostafa Bachar, John M. Karemaker, Franz Kappel (auth.), Jerry J. Batzel, Mostafa Bachar, Franz Kappel (eds.) 9783642328817, 9783642328824, 3642328814, 3642328822 instant download after payment.

This volume synthesizes theoretical and practical aspects of both the mathematical and life science viewpoints needed for modeling of the cardiovascular-respiratory system specifically and physiological systems generally. Theoretical points include model design, model complexity and validation in the light of available data, as well as control theory approaches to feedback delay and Kalman filter applications to parameter identification. State of the art approaches using parameter sensitivity are discussed for enhancing model identifiability through joint analysis of model structure and data. Practical examples illustrate model development at various levels of complexity based on given physiological information. The sensitivity-based approaches for examining model identifiability are illustrated by means of specific modeling examples. The themes presented address the current problem of patient-specific model adaptation in the clinical setting, where data is typically limited.

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