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F7ADIMSM - Modeling and Simulation in Medicine

Code Completion Credits Range Language
F7ADIMSM ZK 20P+8C English
Lecturer:
Raquel Cruz Conceicao (guarantor)
Tutor:
Raquel Cruz Conceicao (guarantor)
Supervisor:
Department of Biomedical Technology
Synopsis:

This Course Unit aims to give students an introductory level knowledge of the methodologies available to model and simulate biological systems and their application in Medicine. At the end of this curricular unit, students should be able to: Identify the usefulness of modeling and simulation of physiological phenomena. Identify the main types of possible models, their advantages and limitations. Distinguish from the applicability of deterministic and stochastic models. Use simple methods to identify model parameters. Know the basic rules of operation and use of Monte Carlo simulation codes. Operate concepts in practical examples using MATLAB.

Requirements:

. The subjects are concluded by an oral examination. The student must elaborate a paper on a given topic together with the exam in case of the controlled self-study. Assessment will be based on active participation in the discussion exercises, reports of group work and presentation of the research protocol conducted during the workshop.

Syllabus of lectures:

Brief Syllabus of Lectures:

1.Physiological complexity and the need for models.

2.Models and the modeling process. What is a model? Why use models? How to model?

3.The process of data modeling. Formulation of models. Validation of modeling. Why and when to model the data.

4.The process of system modeling. (Static models. Linear models. Distributed models. Compartment models. Non-linear models. Time-varying models. Stochastic models.)

5.Model identification. Test signals. Errors. Estimation of parameters. Estimation of signals.

6.Parametric modeling: the identifiability problem and the estimation problem.

7.Validation of modeling methods.

8.Good practices and good modeling.

9.Monte Carlo simulation. (Random variables. Pseudorandom number generator. Inverse Transform method. Monte Carlo integration. Radiation transport: photoelectric effect, Rayleigh scattering).

10.Machine Learning Modeling.

Syllabus of tutorials:

Brief Syllabus of Exercises:

1.Good practices and good modeling – modeling of microwave coil.

2.Support Vector Machine for detection and classification of head stroke.

Study Objective:

This Course Unit aims to give students an introductory level knowledge of the methodologies available to model and simulate biological systems and their application in Medicine. At the end of this curricular unit, students should be able to: Identify the usefulness of modeling and simulation of physiological phenomena. Identify the main types of possible models, their advantages and limitations. Distinguish from the applicability of deterministic and stochastic models. Use simple methods to identify model parameters. Know the basic rules of operation and use of Monte Carlo simulation codes. Operate concepts in practical examples using MATLAB.

Study materials:

Required:

[1] Claudio Cobelli, Ewart Carson, „Introduction to Modeling in Physiology and Medicine“ - Academic Press Series in Biomedical Engineering, Elsevier, 2008, ISBN 978-0-12-160240-6

Recommended:

[1] M. Blomhoj, T.H. Kjeldsen, and J. Ottesen, “Compartment models”, 2005

[2] Luís Peralta, “Introdução aos métodos de simulação Monte Carlo no transporte da radiação”, Faculdade de Ciências, Universidade de Lisboa, 2010

Note:
The course is a part of the following study plans:
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